Posted Updated 43 minutes read (About 6523 words)

Pycaret을 사용한 모델링

Pycaret을 사용한 모델링!

  • 이 노트의 목적
    그 전까지는 사이킷런으로 lightGBM, Catboost 모델링 한 것을 kaggle에 summit 했었다.
    하지만 pycaret을 사용하여 lightGBM과 Catboost를 자동으로 하이퍼 파라미터 튜닝 하여 기존의 점수와 비교해보려 한다.

-결과
*사이킷런으로 스스로 하이퍼파라미터 튜닝한 것 보다, pycaret tune_model로 하이퍼 파라미터 튜닝한 것이 kaggle 점수가 높게 나왔다. *

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# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load

import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

# Input data files are available in the read-only "../input/" directory
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory

import os
for dirname, _, filenames in os.walk('/kaggle/input'):
    for filename in filenames:
        print(os.path.join(dirname, filename))

# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All" 
# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session
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!pip install scikit-learn==0.23.2
Collecting scikit-learn==0.23.2
  Downloading scikit_learn-0.23.2-cp37-cp37m-manylinux1_x86_64.whl (6.8 MB)
     |████████████████████████████████| 6.8 MB 5.4 MB/s 
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Installing collected packages: scikit-learn
  Attempting uninstall: scikit-learn
    Found existing installation: scikit-learn 0.24.1
    Uninstalling scikit-learn-0.24.1:
      Successfully uninstalled scikit-learn-0.24.1
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
pyldavis 3.3.1 requires numpy>=1.20.0, but you have numpy 1.19.5 which is incompatible.
pdpbox 0.2.1 requires matplotlib==3.1.1, but you have matplotlib 3.4.0 which is incompatible.
imbalanced-learn 0.8.0 requires scikit-learn>=0.24, but you have scikit-learn 0.23.2 which is incompatible.
Successfully installed scikit-learn-0.23.2

https://www.kaggle.com/udbhavpangotra/tps-apr21-eda-model

https://www.kaggle.com/hiro5299834/tps-apr-2021-voting-pseudo-labeling

KAGGLE 스터디

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import pandas as pd
import numpy as np
import random
import os

from sklearn.metrics import accuracy_score
from sklearn.preprocessing import LabelEncoder, StandardScaler
from sklearn.model_selection import train_test_split, KFold, StratifiedKFold

import lightgbm as lgb
import catboost as ctb
from sklearn.model_selection import GridSearchCV
from sklearn.tree import DecisionTreeClassifier, export_graphviz

import graphviz
import matplotlib.pyplot as plt
import seaborn as sns

import warnings
warnings.simplefilter('ignore')
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TARGET = 'Survived'

N_ESTIMATORS = 1000
N_SPLITS = 10
SEED = 2021
EARLY_STOPPING_ROUNDS = 100
VERBOSE = 100
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# #랜덤 시드 생성
def set_seed(seed=42):
    random.seed(seed)
    os.environ['PYTHONHASHSEED'] = str(seed)
    np.random.seed(seed)
    
set_seed(SEED)

데이터 전처리

lode data

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train_df = pd.read_csv('../input/tabular-playground-series-apr-2021/train.csv')
test_df = pd.read_csv('../input/tabular-playground-series-apr-2021/test.csv')
submission = pd.read_csv('../input/tabular-playground-series-apr-2021/sample_submission.csv')
#test_df['Survived'] = pd.read_csv("../input/submission-merged3/submission_merged3.csv")['Survived']

all_df = pd.concat([train_df, test_df]).reset_index(drop=True)
#reset_index : 인덱스를 세팅한다. drop=True를 하면 인덱스를 세팅한걸 삭제함. 

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print('Rows and Columns in train dataset:', train_df.shape)
print('Rows and Columns in test dataset:', test_df.shape)
Rows and Columns in train dataset: (100000, 12)
Rows and Columns in test dataset: (100000, 11)

결측치 갯수 출력

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print('Missing values per columns in train dataset')
for col in train_df.columns:
    temp_col = train_df[col].isnull().sum()
    print(f'{col}: {temp_col}')
print()
print('Missing values per columns in test dataset')
for col in test_df.columns:
    temp_col = test_df[col].isnull().sum()
    print(f'{col}: {temp_col}')
Missing values per columns in train dataset
PassengerId: 0
Survived: 0
Pclass: 0
Name: 0
Sex: 0
Age: 3292
SibSp: 0
Parch: 0
Ticket: 4623
Fare: 134
Cabin: 67866
Embarked: 250

Missing values per columns in test dataset
PassengerId: 0
Pclass: 0
Name: 0
Sex: 0
Age: 3487
SibSp: 0
Parch: 0
Ticket: 5181
Fare: 133
Cabin: 70831
Embarked: 277

Filling missing values

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#나이는 나이의 평균치로 채운다.
all_df['Age'] = all_df['Age'].fillna(all_df['Age'].mean())

#cabin은 문자열을 분할하고, 제일 첫번째 글자를 따와서 넣는다. 결측치엔 X를 넣는다.
#strip() : 양쪽 공백을 지운다. 여기서느 x[0]외엔 다 지우는듯. 
all_df['Cabin'] = all_df['Cabin'].fillna('X').map(lambda x: x[0].strip())


#print(all_df['Ticket'].head(10))
#Ticket, fillna with 'X', split string and take first split 
#split() : 문자열 나누기. 디폴트는 ' '이고, 문자를 가진 데이터들이 전부 띄워쓰기로 구분되어있기때문에 가능. 
all_df['Ticket'] = all_df['Ticket'].fillna('X').map(lambda x:str(x).split()[0] if len(str(x).split()) > 1 else 'X')

#pclass에 따른 Fare의 평균을 구해서 dictionary형태로 만든다. 
fare_map = all_df[['Fare', 'Pclass']].dropna().groupby('Pclass').median().to_dict()
#fare의 결측치에 본인 행의 pclass 값을 넣고, 그 값을 fare 평균에 맵핑시킨다.  
all_df['Fare'] = all_df['Fare'].fillna(all_df['Pclass'].map(fare_map['Fare']))
#유독 높은 가격이나 낮은 가격이 있기때문에, 이상치의 영향을 줄이기 위해서 Fare에 log를 취해준다.
all_df['Fare'] = np.log1p(all_df['Fare'])


#항구의 결측치를 X로 채운다. 
all_df['Embarked'] = all_df['Embarked'].fillna('X')

#이름은 성만 사용한다.
all_df['Name'] = all_df['Name'].map(lambda x: x.split(',')[0])

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data_1=all_df.loc[all_df['Pclass']==1].groupby('Ticket')['Ticket'].count().sort_values(ascending=False)
print(data_1)
print()
data_2=all_df.loc[all_df['Pclass']==2].groupby('Ticket')['Ticket'].count().sort_values(ascending=False)
print(data_2)
print()
data_3=all_df.loc[all_df['Pclass']==3].groupby('Ticket')['Ticket'].count().sort_values(ascending=False)
print(data_3)
print()
Ticket
X             36336
PC            16814
C.A.            338
SC/Paris        334
SC/PARIS        260
W./C.           206
S.O.C.          192
S.C./PARIS      191
PP              186
F.C.            183
SC/AH           178
F.C.C.          167
STON/O          163
CA.             161
SOTON/O.Q.      123
A/4             115
A/5.            108
W.E.P.           94
WE/P             92
SOTON/OQ         87
CA               81
STON/O2.         81
A/5              70
C                67
A/4.             66
P/PP             66
SC               59
SOTON/O2         48
A./5.            46
S.O./P.P.        40
A.5.             33
AQ/4             27
A/S              23
SCO/W            19
S.P.             17
SC/A4            16
SW/PP            16
SC/A.3           15
S.O.P.           15
C.A./SOTON       14
A.               14
SO/C             14
S.C./A.4.        14
STON/OQ.         13
W/C              13
LP               11
S.W./PP          11
AQ/3.             8
Fa                7
A4.               6
Name: Ticket, dtype: int64

Ticket
X             31337
A.              997
C.A.            717
SC/PARIS        470
STON/O          387
PC              330
S.O.C.          313
PP              308
SC/AH           284
W./C.           259
SOTON/O.Q.      219
F.C.C.          203
A/5.            200
A/4             152
SC/Paris        135
S.C./PARIS      119
SOTON/O2        112
CA.             107
STON/O2.        106
C               104
F.C.            100
WE/P             92
SOTON/OQ         86
A/5              82
CA               66
W.E.P.           60
A./5.            60
S.O./P.P.        54
P/PP             50
A/4.             46
SCO/W            36
SC               33
A.5.             29
AQ/4             29
LP               25
SC/A.3           20
C.A./SOTON       19
A/S              19
SC/A4            17
Fa               15
S.W./PP          13
SO/C             13
S.C./A.4.        13
STON/OQ.         12
W/C              11
S.P.             10
SW/PP             9
S.O.P.            9
A4.               7
AQ/3.             6
Name: Ticket, dtype: int64

Ticket
X             84781
A.             6420
C.A.           2615
STON/O         1508
A/5.            918
SOTON/O.Q.      719
PP              679
SC/PARIS        642
W./C.           623
PC              595
F.C.C.          541
A/5             420
CA.             368
STON/O2.        363
SC/AH           331
A/4             268
SOTON/O2        264
S.O.C.          231
C               227
SC/Paris        177
S.O./P.P.       177
SOTON/OQ        172
CA              172
W.E.P.          154
F.C.            131
S.C./PARIS      127
A./5.           122
WE/P            121
SC              106
A/4.            104
SCO/W            74
A.5.             72
P/PP             68
SC/A4            67
AQ/4             56
LP               41
Fa               37
STON/OQ.         37
S.W./PP          32
SC/A.3           31
C.A./SOTON       31
SW/PP            30
A/S              28
SO/C             28
AQ/3.            26
S.P.             24
S.C./A.4.        23
S.O.P.           21
W/C              20
A4.              20
Name: Ticket, dtype: int64

인코딩

변수별로 인코딩을 다르게 해준다. 

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label_cols = ['Name', 'Ticket', 'Sex','Pclass','Embarked']
onehot_cols = [ 'Cabin',]
numerical_cols = [ 'Age', 'SibSp', 'Parch', 'Fare']
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#라벨 인코딩 함수. c라는 매개변수를 받아서 맞게 트렌스폼 해준다. 
def label_encoder(c):
    le = LabelEncoder()
    return le.fit_transform(c)
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#StandardScaler(): 평균을 제거하고 데이터를 단위 분산으로 조정한다. 
#그러나 이상치가 있다면 평균과 표준편차에 영향을 미쳐 변환된 데이터의 확산은 매우 달라지게 되는 함수
scaler = StandardScaler()

onehot_encoded_df = pd.get_dummies(all_df[onehot_cols])
label_encoded_df = all_df[label_cols].apply(label_encoder)
numerical_df = pd.DataFrame(scaler.fit_transform(all_df[numerical_cols]), columns=numerical_cols)
target_df = all_df[TARGET]

all_df = pd.concat([numerical_df, label_encoded_df,onehot_encoded_df, target_df], axis=1)
#all_df = pd.concat([numerical_df, label_encoded_df, target_df], axis=1)

모델링

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drop_list=['Survived','Parch']

not pseudo

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train = all_df.iloc[:100000, :]#0개~100000개
test = all_df.iloc[100000:, :] #100000개~ 
#iloc은 정수형 인덱싱
test = test.drop('Survived', axis=1) #test에서 종속변수를 드랍한다. 
model_results = pd.DataFrame()
folds = 5
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y= train.loc[:,'Survived']
X= train.drop(drop_list,axis=1)

pycarot

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caret_train=train.drop('Parch',axis=1)
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!pip install pycaret==2.2.3
Collecting pycaret==2.2.3
  Downloading pycaret-2.2.3-py3-none-any.whl (249 kB)
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Collecting pyod
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Collecting pyLDAvis
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Building wheels for collected packages: alembic, databricks-cli, gunicorn, prometheus-flask-exporter, pyLDAvis, pyod
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[?25h  Created wheel for alembic: filename=alembic-1.4.1-py2.py3-none-any.whl size=158155 sha256=d8f392ff192a5f199c46190c11c01ebd9ee89c41e962f8c53ca200a8f722820d
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Successfully built alembic databricks-cli gunicorn prometheus-flask-exporter pyLDAvis pyod
Installing collected packages: querystring-parser, prometheus-flask-exporter, gunicorn, databricks-cli, alembic, pyod, pyLDAvis, mlflow, imbalanced-learn, pycaret
  Attempting uninstall: alembic
    Found existing installation: alembic 1.5.8
    Uninstalling alembic-1.5.8:
      Successfully uninstalled alembic-1.5.8
  Attempting uninstall: pyLDAvis
    Found existing installation: pyLDAvis 3.3.1
    Uninstalling pyLDAvis-3.3.1:
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  Attempting uninstall: imbalanced-learn
    Found existing installation: imbalanced-learn 0.8.0
    Uninstalling imbalanced-learn-0.8.0:
      Successfully uninstalled imbalanced-learn-0.8.0
Successfully installed alembic-1.4.1 databricks-cli-0.14.3 gunicorn-20.1.0 imbalanced-learn-0.7.0 mlflow-1.16.0 prometheus-flask-exporter-0.18.1 pyLDAvis-3.2.2 pycaret-2.2.3 pyod-0.8.8 querystring-parser-1.2.4

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from pycaret.utils import version
import sklearn
print("pycaret version:", version())
print("sklearn version:", sklearn.__version__)
pycaret version: 2.2.3
sklearn version: 0.23.2

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import pycaret.classification 
# from pycaret.regression import *
# reg1 = setup(data = train,target = 'Survived')
from pycaret.classification import *
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4
caret_train.loc[:,'Embarked'].unique()
caret_train.loc[:,'Pclass'].unique()
caret_train.loc[:,'Sex'].unique()

array([1, 0])

1
category_caret={'Sex':['0','1'],'Pclass':['0','1','2'], 'Embarked':['0','1','2','3']}
1
2
3
4
5
6
7
8
9
10
11
setup(data = caret_train, 
      target = 'Survived',
      ordinal_features= category_caret,
      #numeric_imputation = 'Age','SibSp','Name','Ticket','Fare',
      fold=5,
      silent = True,
      session_id=1,
      #data_split_shuffle=True
      fold_shuffle=True

     )
            <tr>
                    <th id="T_4e1bb_level0_row0" class="row_heading level0 row0" >0</th>
                    <td id="T_4e1bb_row0_col0" class="data row0 col0" >session_id</td>
                    <td id="T_4e1bb_row0_col1" class="data row0 col1" >1</td>
        </tr>
        <tr>
                    <th id="T_4e1bb_level0_row1" class="row_heading level0 row1" >1</th>
                    <td id="T_4e1bb_row1_col0" class="data row1 col0" >Target</td>
                    <td id="T_4e1bb_row1_col1" class="data row1 col1" >Survived</td>
        </tr>
        <tr>
                    <th id="T_4e1bb_level0_row2" class="row_heading level0 row2" >2</th>
                    <td id="T_4e1bb_row2_col0" class="data row2 col0" >Target Type</td>
                    <td id="T_4e1bb_row2_col1" class="data row2 col1" >Binary</td>
        </tr>
        <tr>
                    <th id="T_4e1bb_level0_row3" class="row_heading level0 row3" >3</th>
                    <td id="T_4e1bb_row3_col0" class="data row3 col0" >Label Encoded</td>
                    <td id="T_4e1bb_row3_col1" class="data row3 col1" >0.0: 0, 1.0: 1</td>
        </tr>
        <tr>
                    <th id="T_4e1bb_level0_row4" class="row_heading level0 row4" >4</th>
                    <td id="T_4e1bb_row4_col0" class="data row4 col0" >Original Data</td>
                    <td id="T_4e1bb_row4_col1" class="data row4 col1" >(100000, 18)</td>
        </tr>
        <tr>
                    <th id="T_4e1bb_level0_row5" class="row_heading level0 row5" >5</th>
                    <td id="T_4e1bb_row5_col0" class="data row5 col0" >Missing Values</td>
                    <td id="T_4e1bb_row5_col1" class="data row5 col1" >False</td>
        </tr>
        <tr>
                    <th id="T_4e1bb_level0_row6" class="row_heading level0 row6" >6</th>
                    <td id="T_4e1bb_row6_col0" class="data row6 col0" >Numeric Features</td>
                    <td id="T_4e1bb_row6_col1" class="data row6 col1" >14</td>
        </tr>
        <tr>
                    <th id="T_4e1bb_level0_row7" class="row_heading level0 row7" >7</th>
                    <td id="T_4e1bb_row7_col0" class="data row7 col0" >Categorical Features</td>
                    <td id="T_4e1bb_row7_col1" class="data row7 col1" >3</td>
        </tr>
        <tr>
                    <th id="T_4e1bb_level0_row8" class="row_heading level0 row8" >8</th>
                    <td id="T_4e1bb_row8_col0" class="data row8 col0" >Ordinal Features</td>
                    <td id="T_4e1bb_row8_col1" class="data row8 col1" >True</td>
        </tr>
        <tr>
                    <th id="T_4e1bb_level0_row9" class="row_heading level0 row9" >9</th>
                    <td id="T_4e1bb_row9_col0" class="data row9 col0" >High Cardinality Features</td>
                    <td id="T_4e1bb_row9_col1" class="data row9 col1" >False</td>
        </tr>
        <tr>
                    <th id="T_4e1bb_level0_row10" class="row_heading level0 row10" >10</th>
                    <td id="T_4e1bb_row10_col0" class="data row10 col0" >High Cardinality Method</td>
                    <td id="T_4e1bb_row10_col1" class="data row10 col1" >None</td>
        </tr>
        <tr>
                    <th id="T_4e1bb_level0_row11" class="row_heading level0 row11" >11</th>
                    <td id="T_4e1bb_row11_col0" class="data row11 col0" >Transformed Train Set</td>
                    <td id="T_4e1bb_row11_col1" class="data row11 col1" >(69999, 17)</td>
        </tr>
        <tr>
                    <th id="T_4e1bb_level0_row12" class="row_heading level0 row12" >12</th>
                    <td id="T_4e1bb_row12_col0" class="data row12 col0" >Transformed Test Set</td>
                    <td id="T_4e1bb_row12_col1" class="data row12 col1" >(30001, 17)</td>
        </tr>
        <tr>
                    <th id="T_4e1bb_level0_row13" class="row_heading level0 row13" >13</th>
                    <td id="T_4e1bb_row13_col0" class="data row13 col0" >Shuffle Train-Test</td>
                    <td id="T_4e1bb_row13_col1" class="data row13 col1" >True</td>
        </tr>
        <tr>
                    <th id="T_4e1bb_level0_row14" class="row_heading level0 row14" >14</th>
                    <td id="T_4e1bb_row14_col0" class="data row14 col0" >Stratify Train-Test</td>
                    <td id="T_4e1bb_row14_col1" class="data row14 col1" >False</td>
        </tr>
        <tr>
                    <th id="T_4e1bb_level0_row15" class="row_heading level0 row15" >15</th>
                    <td id="T_4e1bb_row15_col0" class="data row15 col0" >Fold Generator</td>
                    <td id="T_4e1bb_row15_col1" class="data row15 col1" >StratifiedKFold</td>
        </tr>
        <tr>
                    <th id="T_4e1bb_level0_row16" class="row_heading level0 row16" >16</th>
                    <td id="T_4e1bb_row16_col0" class="data row16 col0" >Fold Number</td>
                    <td id="T_4e1bb_row16_col1" class="data row16 col1" >5</td>
        </tr>
        <tr>
                    <th id="T_4e1bb_level0_row17" class="row_heading level0 row17" >17</th>
                    <td id="T_4e1bb_row17_col0" class="data row17 col0" >CPU Jobs</td>
                    <td id="T_4e1bb_row17_col1" class="data row17 col1" >-1</td>
        </tr>
        <tr>
                    <th id="T_4e1bb_level0_row18" class="row_heading level0 row18" >18</th>
                    <td id="T_4e1bb_row18_col0" class="data row18 col0" >Use GPU</td>
                    <td id="T_4e1bb_row18_col1" class="data row18 col1" >False</td>
        </tr>
        <tr>
                    <th id="T_4e1bb_level0_row19" class="row_heading level0 row19" >19</th>
                    <td id="T_4e1bb_row19_col0" class="data row19 col0" >Log Experiment</td>
                    <td id="T_4e1bb_row19_col1" class="data row19 col1" >False</td>
        </tr>
        <tr>
                    <th id="T_4e1bb_level0_row20" class="row_heading level0 row20" >20</th>
                    <td id="T_4e1bb_row20_col0" class="data row20 col0" >Experiment Name</td>
                    <td id="T_4e1bb_row20_col1" class="data row20 col1" >clf-default-name</td>
        </tr>
        <tr>
                    <th id="T_4e1bb_level0_row21" class="row_heading level0 row21" >21</th>
                    <td id="T_4e1bb_row21_col0" class="data row21 col0" >USI</td>
                    <td id="T_4e1bb_row21_col1" class="data row21 col1" >9220</td>
        </tr>
        <tr>
                    <th id="T_4e1bb_level0_row22" class="row_heading level0 row22" >22</th>
                    <td id="T_4e1bb_row22_col0" class="data row22 col0" >Imputation Type</td>
                    <td id="T_4e1bb_row22_col1" class="data row22 col1" >simple</td>
        </tr>
        <tr>
                    <th id="T_4e1bb_level0_row23" class="row_heading level0 row23" >23</th>
                    <td id="T_4e1bb_row23_col0" class="data row23 col0" >Iterative Imputation Iteration</td>
                    <td id="T_4e1bb_row23_col1" class="data row23 col1" >None</td>
        </tr>
        <tr>
                    <th id="T_4e1bb_level0_row24" class="row_heading level0 row24" >24</th>
                    <td id="T_4e1bb_row24_col0" class="data row24 col0" >Numeric Imputer</td>
                    <td id="T_4e1bb_row24_col1" class="data row24 col1" >mean</td>
        </tr>
        <tr>
                    <th id="T_4e1bb_level0_row25" class="row_heading level0 row25" >25</th>
                    <td id="T_4e1bb_row25_col0" class="data row25 col0" >Iterative Imputation Numeric Model</td>
                    <td id="T_4e1bb_row25_col1" class="data row25 col1" >None</td>
        </tr>
        <tr>
                    <th id="T_4e1bb_level0_row26" class="row_heading level0 row26" >26</th>
                    <td id="T_4e1bb_row26_col0" class="data row26 col0" >Categorical Imputer</td>
                    <td id="T_4e1bb_row26_col1" class="data row26 col1" >constant</td>
        </tr>
        <tr>
                    <th id="T_4e1bb_level0_row27" class="row_heading level0 row27" >27</th>
                    <td id="T_4e1bb_row27_col0" class="data row27 col0" >Iterative Imputation Categorical Model</td>
                    <td id="T_4e1bb_row27_col1" class="data row27 col1" >None</td>
        </tr>
        <tr>
                    <th id="T_4e1bb_level0_row28" class="row_heading level0 row28" >28</th>
                    <td id="T_4e1bb_row28_col0" class="data row28 col0" >Unknown Categoricals Handling</td>
                    <td id="T_4e1bb_row28_col1" class="data row28 col1" >least_frequent</td>
        </tr>
        <tr>
                    <th id="T_4e1bb_level0_row29" class="row_heading level0 row29" >29</th>
                    <td id="T_4e1bb_row29_col0" class="data row29 col0" >Normalize</td>
                    <td id="T_4e1bb_row29_col1" class="data row29 col1" >False</td>
        </tr>
        <tr>
                    <th id="T_4e1bb_level0_row30" class="row_heading level0 row30" >30</th>
                    <td id="T_4e1bb_row30_col0" class="data row30 col0" >Normalize Method</td>
                    <td id="T_4e1bb_row30_col1" class="data row30 col1" >None</td>
        </tr>
        <tr>
                    <th id="T_4e1bb_level0_row31" class="row_heading level0 row31" >31</th>
                    <td id="T_4e1bb_row31_col0" class="data row31 col0" >Transformation</td>
                    <td id="T_4e1bb_row31_col1" class="data row31 col1" >False</td>
        </tr>
        <tr>
                    <th id="T_4e1bb_level0_row32" class="row_heading level0 row32" >32</th>
                    <td id="T_4e1bb_row32_col0" class="data row32 col0" >Transformation Method</td>
                    <td id="T_4e1bb_row32_col1" class="data row32 col1" >None</td>
        </tr>
        <tr>
                    <th id="T_4e1bb_level0_row33" class="row_heading level0 row33" >33</th>
                    <td id="T_4e1bb_row33_col0" class="data row33 col0" >PCA</td>
                    <td id="T_4e1bb_row33_col1" class="data row33 col1" >False</td>
        </tr>
        <tr>
                    <th id="T_4e1bb_level0_row34" class="row_heading level0 row34" >34</th>
                    <td id="T_4e1bb_row34_col0" class="data row34 col0" >PCA Method</td>
                    <td id="T_4e1bb_row34_col1" class="data row34 col1" >None</td>
        </tr>
        <tr>
                    <th id="T_4e1bb_level0_row35" class="row_heading level0 row35" >35</th>
                    <td id="T_4e1bb_row35_col0" class="data row35 col0" >PCA Components</td>
                    <td id="T_4e1bb_row35_col1" class="data row35 col1" >None</td>
        </tr>
        <tr>
                    <th id="T_4e1bb_level0_row36" class="row_heading level0 row36" >36</th>
                    <td id="T_4e1bb_row36_col0" class="data row36 col0" >Ignore Low Variance</td>
                    <td id="T_4e1bb_row36_col1" class="data row36 col1" >False</td>
        </tr>
        <tr>
                    <th id="T_4e1bb_level0_row37" class="row_heading level0 row37" >37</th>
                    <td id="T_4e1bb_row37_col0" class="data row37 col0" >Combine Rare Levels</td>
                    <td id="T_4e1bb_row37_col1" class="data row37 col1" >False</td>
        </tr>
        <tr>
                    <th id="T_4e1bb_level0_row38" class="row_heading level0 row38" >38</th>
                    <td id="T_4e1bb_row38_col0" class="data row38 col0" >Rare Level Threshold</td>
                    <td id="T_4e1bb_row38_col1" class="data row38 col1" >None</td>
        </tr>
        <tr>
                    <th id="T_4e1bb_level0_row39" class="row_heading level0 row39" >39</th>
                    <td id="T_4e1bb_row39_col0" class="data row39 col0" >Numeric Binning</td>
                    <td id="T_4e1bb_row39_col1" class="data row39 col1" >False</td>
        </tr>
        <tr>
                    <th id="T_4e1bb_level0_row40" class="row_heading level0 row40" >40</th>
                    <td id="T_4e1bb_row40_col0" class="data row40 col0" >Remove Outliers</td>
                    <td id="T_4e1bb_row40_col1" class="data row40 col1" >False</td>
        </tr>
        <tr>
                    <th id="T_4e1bb_level0_row41" class="row_heading level0 row41" >41</th>
                    <td id="T_4e1bb_row41_col0" class="data row41 col0" >Outliers Threshold</td>
                    <td id="T_4e1bb_row41_col1" class="data row41 col1" >None</td>
        </tr>
        <tr>
                    <th id="T_4e1bb_level0_row42" class="row_heading level0 row42" >42</th>
                    <td id="T_4e1bb_row42_col0" class="data row42 col0" >Remove Multicollinearity</td>
                    <td id="T_4e1bb_row42_col1" class="data row42 col1" >False</td>
        </tr>
        <tr>
                    <th id="T_4e1bb_level0_row43" class="row_heading level0 row43" >43</th>
                    <td id="T_4e1bb_row43_col0" class="data row43 col0" >Multicollinearity Threshold</td>
                    <td id="T_4e1bb_row43_col1" class="data row43 col1" >None</td>
        </tr>
        <tr>
                    <th id="T_4e1bb_level0_row44" class="row_heading level0 row44" >44</th>
                    <td id="T_4e1bb_row44_col0" class="data row44 col0" >Clustering</td>
                    <td id="T_4e1bb_row44_col1" class="data row44 col1" >False</td>
        </tr>
        <tr>
                    <th id="T_4e1bb_level0_row45" class="row_heading level0 row45" >45</th>
                    <td id="T_4e1bb_row45_col0" class="data row45 col0" >Clustering Iteration</td>
                    <td id="T_4e1bb_row45_col1" class="data row45 col1" >None</td>
        </tr>
        <tr>
                    <th id="T_4e1bb_level0_row46" class="row_heading level0 row46" >46</th>
                    <td id="T_4e1bb_row46_col0" class="data row46 col0" >Polynomial Features</td>
                    <td id="T_4e1bb_row46_col1" class="data row46 col1" >False</td>
        </tr>
        <tr>
                    <th id="T_4e1bb_level0_row47" class="row_heading level0 row47" >47</th>
                    <td id="T_4e1bb_row47_col0" class="data row47 col0" >Polynomial Degree</td>
                    <td id="T_4e1bb_row47_col1" class="data row47 col1" >None</td>
        </tr>
        <tr>
                    <th id="T_4e1bb_level0_row48" class="row_heading level0 row48" >48</th>
                    <td id="T_4e1bb_row48_col0" class="data row48 col0" >Trignometry Features</td>
                    <td id="T_4e1bb_row48_col1" class="data row48 col1" >False</td>
        </tr>
        <tr>
                    <th id="T_4e1bb_level0_row49" class="row_heading level0 row49" >49</th>
                    <td id="T_4e1bb_row49_col0" class="data row49 col0" >Polynomial Threshold</td>
                    <td id="T_4e1bb_row49_col1" class="data row49 col1" >None</td>
        </tr>
        <tr>
                    <th id="T_4e1bb_level0_row50" class="row_heading level0 row50" >50</th>
                    <td id="T_4e1bb_row50_col0" class="data row50 col0" >Group Features</td>
                    <td id="T_4e1bb_row50_col1" class="data row50 col1" >False</td>
        </tr>
        <tr>
                    <th id="T_4e1bb_level0_row51" class="row_heading level0 row51" >51</th>
                    <td id="T_4e1bb_row51_col0" class="data row51 col0" >Feature Selection</td>
                    <td id="T_4e1bb_row51_col1" class="data row51 col1" >False</td>
        </tr>
        <tr>
                    <th id="T_4e1bb_level0_row52" class="row_heading level0 row52" >52</th>
                    <td id="T_4e1bb_row52_col0" class="data row52 col0" >Features Selection Threshold</td>
                    <td id="T_4e1bb_row52_col1" class="data row52 col1" >None</td>
        </tr>
        <tr>
                    <th id="T_4e1bb_level0_row53" class="row_heading level0 row53" >53</th>
                    <td id="T_4e1bb_row53_col0" class="data row53 col0" >Feature Interaction</td>
                    <td id="T_4e1bb_row53_col1" class="data row53 col1" >False</td>
        </tr>
        <tr>
                    <th id="T_4e1bb_level0_row54" class="row_heading level0 row54" >54</th>
                    <td id="T_4e1bb_row54_col0" class="data row54 col0" >Feature Ratio</td>
                    <td id="T_4e1bb_row54_col1" class="data row54 col1" >False</td>
        </tr>
        <tr>
                    <th id="T_4e1bb_level0_row55" class="row_heading level0 row55" >55</th>
                    <td id="T_4e1bb_row55_col0" class="data row55 col0" >Interaction Threshold</td>
                    <td id="T_4e1bb_row55_col1" class="data row55 col1" >None</td>
        </tr>
        <tr>
                    <th id="T_4e1bb_level0_row56" class="row_heading level0 row56" >56</th>
                    <td id="T_4e1bb_row56_col0" class="data row56 col0" >Fix Imbalance</td>
                    <td id="T_4e1bb_row56_col1" class="data row56 col1" >False</td>
        </tr>
        <tr>
                    <th id="T_4e1bb_level0_row57" class="row_heading level0 row57" >57</th>
                    <td id="T_4e1bb_row57_col0" class="data row57 col0" >Fix Imbalance Method</td>
                    <td id="T_4e1bb_row57_col1" class="data row57 col1" >SMOTE</td>
        </tr>
</tbody></table>





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  'html_param',
  'imputation_classifier',
  'imputation_regressor',
  'iterative_imputation_iters_param',
  'log_plots_param',
  'logging_param',
  'master_model_container',
  'n_jobs_param',
  'prep_pipe',
  'pycaret_globals',
  'seed',
  'stratify_param',
  'target_param',
  'transform_target_method_param',
  'transform_target_param',
  'y',
  'y_test',
  'y_train'},
 'Survived',
 True,
 -1,
             Age     SibSp      Fare     Name  Ticket  Sex  Pclass  Embarked  \
 62017 -1.786066 -0.539572 -0.425227  23058.0    38.0  0.0     1.0       2.0   
 5005   0.274926 -0.539572  0.041264   9979.0    49.0  1.0     1.0       2.0   
 56849 -1.361744 -0.539572  0.215883  14486.0    49.0  0.0     1.0       2.0   
 42434  1.426657 -0.539572  1.209948   8350.0    21.0  0.0     0.0       0.0   
 54712 -1.725448 -0.539572 -0.906172  18642.0     0.0  0.0     2.0       2.0   
 ...         ...       ...       ...      ...     ...  ...     ...       ...   
 50057  1.608510 -0.539572  1.938114   4224.0    49.0  1.0     0.0       2.0   
 98047 -0.149396  0.680848 -0.914255  21294.0    49.0  1.0     2.0       2.0   
 5192  -0.634335 -0.539572 -1.037225  24329.0    49.0  1.0     1.0       2.0   
 77708  0.820483 -0.539572  2.156857   5150.0    49.0  0.0     0.0       0.0   
 98539  0.396161 -0.539572  2.729733  25222.0    49.0  1.0     0.0       1.0   
 
        Cabin_A  Cabin_B  Cabin_C  Cabin_D  Cabin_E  Cabin_F  Cabin_G  Cabin_T  \
 62017        0        0        0        0        0        0        0        0   
 5005         0        0        0        0        0        0        0        0   
 56849        0        0        0        0        0        0        0        0   
 42434        0        0        1        0        0        0        0        0   
 54712        0        0        0        0        0        0        0        0   
 ...        ...      ...      ...      ...      ...      ...      ...      ...   
 50057        1        0        0        0        0        0        0        0   
 98047        0        0        0        0        0        0        0        0   
 5192         0        0        0        0        0        0        0        0   
 77708        0        1        0        0        0        0        0        0   
 98539        1        0        0        0        0        0        0        0   
 
        Cabin_X  
 62017        1  
 5005         1  
 56849        1  
 42434        0  
 54712        1  
 ...        ...  
 50057        0  
 98047        1  
 5192         1  
 77708        0  
 98539        0  
 
 [69999 rows x 17 columns],
 Pipeline(memory=None, steps=[('empty_step', 'passthrough')], verbose=False),
 'lightgbm',
 False,
 Pipeline(memory=None,
          steps=[('dtypes',
                  DataTypes_Auto_infer(categorical_features=[],
                                       display_types=False, features_todrop=[],
                                       id_columns=[],
                                       ml_usecase='classification',
                                       numerical_features=[], target='Survived',
                                       time_features=[])),
                 ('imputer',
                  Simple_Imputer(categorical_strategy='not_available',
                                 fill_value_categorical=None,
                                 fill_value_numerical=None,
                                 numeric_st...
                 ('scaling', 'passthrough'), ('P_transform', 'passthrough'),
                 ('binn', 'passthrough'), ('rem_outliers', 'passthrough'),
                 ('cluster_all', 'passthrough'),
                 ('dummy', Dummify(target='Survived')),
                 ('fix_perfect', Remove_100(target='Survived')),
                 ('clean_names', Clean_Colum_Names()),
                 ('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),
                 ('dfs', 'passthrough'), ('pca', 'passthrough')],
          verbose=False),
 [],
 '9220',
 0        1
 1        0
 2        0
 3        0
 4        1
         ..
 99995    1
 99996    0
 99997    0
 99998    0
 99999    0
 Name: Survived, Length: 100000, dtype: int64,
 False,
 [],
 'box-cox',
 {'lr': <pycaret.containers.models.classification.LogisticRegressionClassifierContainer at 0x7fdbc078af90>,
  'knn': <pycaret.containers.models.classification.KNeighborsClassifierContainer at 0x7fdbc078ae50>,
  'nb': <pycaret.containers.models.classification.GaussianNBClassifierContainer at 0x7fdbc078ad10>,
  'dt': <pycaret.containers.models.classification.DecisionTreeClassifierContainer at 0x7fdbc078ae90>,
  'svm': <pycaret.containers.models.classification.SGDClassifierContainer at 0x7fdbc078aa10>,
  'rbfsvm': <pycaret.containers.models.classification.SVCClassifierContainer at 0x7fdbc078a710>,
  'gpc': <pycaret.containers.models.classification.GaussianProcessClassifierContainer at 0x7fdbc078a8d0>,
  'mlp': <pycaret.containers.models.classification.MLPClassifierContainer at 0x7fdbc078a550>,
  'ridge': <pycaret.containers.models.classification.RidgeClassifierContainer at 0x7fdbc078a2d0>,
  'rf': <pycaret.containers.models.classification.RandomForestClassifierContainer at 0x7fdbc078a990>,
  'qda': <pycaret.containers.models.classification.QuadraticDiscriminantAnalysisContainer at 0x7fdbc078b210>,
  'ada': <pycaret.containers.models.classification.AdaBoostClassifierContainer at 0x7fdbc078b250>,
  'gbc': <pycaret.containers.models.classification.GradientBoostingClassifierContainer at 0x7fdbc078b4d0>,
  'lda': <pycaret.containers.models.classification.LinearDiscriminantAnalysisContainer at 0x7fdbc078b7d0>,
  'et': <pycaret.containers.models.classification.ExtraTreesClassifierContainer at 0x7fdbc078b8d0>,
  'xgboost': <pycaret.containers.models.classification.XGBClassifierContainer at 0x7fdbc078bc10>,
  'lightgbm': <pycaret.containers.models.classification.LGBMClassifierContainer at 0x7fdbc0f33090>,
  'catboost': <pycaret.containers.models.classification.CatBoostClassifierContainer at 0x7fdbc078afd0>,
  'Bagging': <pycaret.containers.models.classification.BaggingClassifierContainer at 0x7fdbc0762ed0>,
  'Stacking': <pycaret.containers.models.classification.StackingClassifierContainer at 0x7fdbc0762f10>,
  'Voting': <pycaret.containers.models.classification.VotingClassifierContainer at 0x7fdbc0f33190>,
  'CalibratedCV': <pycaret.containers.models.classification.CalibratedClassifierCVContainer at 0x7fdbc0f334d0>},
                 Age     SibSp      Fare   Name  Ticket  Sex  Pclass  Embarked  \
 0     -8.614253e-16  1.901268  0.134351  17441      49    1       0         2   
 1     -8.614253e-16 -0.539572 -0.533837   3063      49    1       2         2   
 2     -2.069149e+00  0.680848  1.070483  17798      14    1       2         2   
 3     -9.374220e-01 -0.539572 -0.555506  12742       0    1       2         2   
 4     -5.737175e-01 -0.539572 -1.023540   2335      49    1       2         2   
 ...             ...       ...       ...    ...     ...  ...     ...       ...   
 99995  1.669127e+00 -0.539572 -0.434567   1590      21    0       1         0   
 99996  1.911597e+00 -0.539572 -0.698959   2992      49    1       1         2   
 99997  1.536915e-01 -0.539572 -0.802137   4219      49    1       2         2   
 99998  1.002335e+00 -0.539572  0.259408   3941      49    1       2         2   
 99999  1.244805e+00 -0.539572 -0.492531   7055      49    1       2         2   
 
        Cabin_A  Cabin_B  Cabin_C  Cabin_D  Cabin_E  Cabin_F  Cabin_G  Cabin_T  \
 0            0        0        1        0        0        0        0        0   
 1            0        0        0        0        0        0        0        0   
 2            0        0        0        0        0        0        0        0   
 3            0        0        0        0        0        0        0        0   
 4            0        0        0        0        0        0        0        0   
 ...        ...      ...      ...      ...      ...      ...      ...      ...   
 99995        0        0        0        1        0        0        0        0   
 99996        0        0        0        0        0        0        0        0   
 99997        0        0        0        0        0        0        0        0   
 99998        0        0        0        0        0        0        0        0   
 99999        0        0        0        0        0        0        0        0   
 
        Cabin_X  Survived  
 0            0       1.0  
 1            1       0.0  
 2            1       0.0  
 3            1       0.0  
 4            1       1.0  
 ...        ...       ...  
 99995        0       1.0  
 99996        1       0.0  
 99997        1       0.0  
 99998        1       0.0  
 99999        1       0.0  
 
 [100000 rows x 18 columns],
 5,
 [('Setup Config',
                                  Description             Value
   0                               session_id                 1
   1                                   Target          Survived
   2                              Target Type            Binary
   3                            Label Encoded    0.0: 0, 1.0: 1
   4                            Original Data      (100000, 18)
   5                           Missing Values             False
   6                         Numeric Features                14
   7                     Categorical Features                 3
   8                         Ordinal Features              True
   9                High Cardinality Features             False
   10                 High Cardinality Method              None
   11                   Transformed Train Set       (69999, 17)
   12                    Transformed Test Set       (30001, 17)
   13                      Shuffle Train-Test              True
   14                     Stratify Train-Test             False
   15                          Fold Generator   StratifiedKFold
   16                             Fold Number                 5
   17                                CPU Jobs                -1
   18                                 Use GPU             False
   19                          Log Experiment             False
   20                         Experiment Name  clf-default-name
   21                                     USI              9220
   22                         Imputation Type            simple
   23          Iterative Imputation Iteration              None
   24                         Numeric Imputer              mean
   25      Iterative Imputation Numeric Model              None
   26                     Categorical Imputer          constant
   27  Iterative Imputation Categorical Model              None
   28           Unknown Categoricals Handling    least_frequent
   29                               Normalize             False
   30                        Normalize Method              None
   31                          Transformation             False
   32                   Transformation Method              None
   33                                     PCA             False
   34                              PCA Method              None
   35                          PCA Components              None
   36                     Ignore Low Variance             False
   37                     Combine Rare Levels             False
   38                    Rare Level Threshold              None
   39                         Numeric Binning             False
   40                         Remove Outliers             False
   41                      Outliers Threshold              None
   42                Remove Multicollinearity             False
   43             Multicollinearity Threshold              None
   44                              Clustering             False
   45                    Clustering Iteration              None
   46                     Polynomial Features             False
   47                       Polynomial Degree              None
   48                    Trignometry Features             False
   49                    Polynomial Threshold              None
   50                          Group Features             False
   51                       Feature Selection             False
   52            Features Selection Threshold              None
   53                     Feature Interaction             False
   54                           Feature Ratio             False
   55                   Interaction Threshold              None
   56                           Fix Imbalance             False
   57                    Fix Imbalance Method             SMOTE),
  ('X_training Set',
               Age     SibSp      Fare     Name  Ticket  Sex  Pclass  Embarked  \
   62017 -1.786066 -0.539572 -0.425227  23058.0    38.0  0.0     1.0       2.0   
   5005   0.274926 -0.539572  0.041264   9979.0    49.0  1.0     1.0       2.0   
   56849 -1.361744 -0.539572  0.215883  14486.0    49.0  0.0     1.0       2.0   
   42434  1.426657 -0.539572  1.209948   8350.0    21.0  0.0     0.0       0.0   
   54712 -1.725448 -0.539572 -0.906172  18642.0     0.0  0.0     2.0       2.0   
   ...         ...       ...       ...      ...     ...  ...     ...       ...   
   50057  1.608510 -0.539572  1.938114   4224.0    49.0  1.0     0.0       2.0   
   98047 -0.149396  0.680848 -0.914255  21294.0    49.0  1.0     2.0       2.0   
   5192  -0.634335 -0.539572 -1.037225  24329.0    49.0  1.0     1.0       2.0   
   77708  0.820483 -0.539572  2.156857   5150.0    49.0  0.0     0.0       0.0   
   98539  0.396161 -0.539572  2.729733  25222.0    49.0  1.0     0.0       1.0   
   
          Cabin_A  Cabin_B  Cabin_C  Cabin_D  Cabin_E  Cabin_F  Cabin_G  Cabin_T  \
   62017        0        0        0        0        0        0        0        0   
   5005         0        0        0        0        0        0        0        0   
   56849        0        0        0        0        0        0        0        0   
   42434        0        0        1        0        0        0        0        0   
   54712        0        0        0        0        0        0        0        0   
   ...        ...      ...      ...      ...      ...      ...      ...      ...   
   50057        1        0        0        0        0        0        0        0   
   98047        0        0        0        0        0        0        0        0   
   5192         0        0        0        0        0        0        0        0   
   77708        0        1        0        0        0        0        0        0   
   98539        1        0        0        0        0        0        0        0   
   
          Cabin_X  
   62017        1  
   5005         1  
   56849        1  
   42434        0  
   54712        1  
   ...        ...  
   50057        0  
   98047        1  
   5192         1  
   77708        0  
   98539        0  
   
   [69999 rows x 17 columns]),
  ('y_training Set',
   62017    0
   5005     0
   56849    1
   42434    1
   54712    0
           ..
   50057    1
   98047    0
   5192     0
   77708    0
   98539    1
   Name: Survived, Length: 69999, dtype: int64),
  ('X_test Set',
               Age     SibSp      Fare     Name  Ticket  Sex  Pclass  Embarked  \
   43660  1.244805 -0.539572  0.820679  25313.0    49.0  0.0     0.0       2.0   
   87278 -0.210013 -0.539572 -0.555506   3156.0    49.0  1.0     0.0       0.0   
   14317  0.941718  0.680848  1.561942  11588.0    49.0  1.0     0.0       2.0   
   81932  0.638631 -0.539572  0.050516  16175.0    21.0  1.0     0.0       0.0   
   95321 -0.694952  0.680848  0.063476  10196.0    49.0  1.0     0.0       2.0   
   ...         ...       ...       ...      ...     ...  ...     ...       ...   
   42287 -1.179892 -0.539572 -0.463126  11629.0    49.0  1.0     2.0       2.0   
   4967  -0.452483 -0.539572  0.265297   3394.0    49.0  1.0     1.0       0.0   
   47725  1.244805 -0.539572 -0.364529  19040.0    49.0  1.0     1.0       2.0   
   42348 -0.694952 -0.539572 -1.197666  15816.0    49.0  1.0     2.0       2.0   
   80630 -0.452483 -0.539572 -0.900153  24505.0    49.0  1.0     2.0       2.0   
   
          Cabin_A  Cabin_B  Cabin_C  Cabin_D  Cabin_E  Cabin_F  Cabin_G  Cabin_T  \
   43660        0        0        1        0        0        0        0        0   
   87278        0        0        1        0        0        0        0        0   
   14317        1        0        0        0        0        0        0        0   
   81932        1        0        0        0        0        0        0        0   
   95321        0        0        1        0        0        0        0        0   
   ...        ...      ...      ...      ...      ...      ...      ...      ...   
   42287        0        0        0        0        0        0        0        0   
   4967         0        0        0        0        0        0        0        0   
   47725        0        0        0        0        0        0        0        0   
   42348        0        0        0        0        0        0        0        0   
   80630        0        0        0        0        0        0        0        0   
   
          Cabin_X  
   43660        0  
   87278        0  
   14317        0  
   81932        0  
   95321        0  
   ...        ...  
   42287        1  
   4967         1  
   47725        1  
   42348        1  
   80630        1  
   
   [30001 rows x 17 columns]),
  ('y_test Set',
   43660    1
   87278    0
   14317    0
   81932    1
   95321    0
           ..
   42287    0
   4967     1
   47725    0
   42348    0
   80630    0
   Name: Survived, Length: 30001, dtype: int64),
  ('Transformation Pipeline',
   Pipeline(memory=None,
            steps=[('dtypes',
                    DataTypes_Auto_infer(categorical_features=[],
                                         display_types=False, features_todrop=[],
                                         id_columns=[],
                                         ml_usecase='classification',
                                         numerical_features=[], target='Survived',
                                         time_features=[])),
                   ('imputer',
                    Simple_Imputer(categorical_strategy='not_available',
                                   fill_value_categorical=None,
                                   fill_value_numerical=None,
                                   numeric_st...
                   ('scaling', 'passthrough'), ('P_transform', 'passthrough'),
                   ('binn', 'passthrough'), ('rem_outliers', 'passthrough'),
                   ('cluster_all', 'passthrough'),
                   ('dummy', Dummify(target='Survived')),
                   ('fix_perfect', Remove_100(target='Survived')),
                   ('clean_names', Clean_Colum_Names()),
                   ('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),
                   ('dfs', 'passthrough'), ('pca', 'passthrough')],
            verbose=False))],
 'lightgbm',
 False,
             Age     SibSp      Fare     Name  Ticket  Sex  Pclass  Embarked  \
 43660  1.244805 -0.539572  0.820679  25313.0    49.0  0.0     0.0       2.0   
 87278 -0.210013 -0.539572 -0.555506   3156.0    49.0  1.0     0.0       0.0   
 14317  0.941718  0.680848  1.561942  11588.0    49.0  1.0     0.0       2.0   
 81932  0.638631 -0.539572  0.050516  16175.0    21.0  1.0     0.0       0.0   
 95321 -0.694952  0.680848  0.063476  10196.0    49.0  1.0     0.0       2.0   
 ...         ...       ...       ...      ...     ...  ...     ...       ...   
 42287 -1.179892 -0.539572 -0.463126  11629.0    49.0  1.0     2.0       2.0   
 4967  -0.452483 -0.539572  0.265297   3394.0    49.0  1.0     1.0       0.0   
 47725  1.244805 -0.539572 -0.364529  19040.0    49.0  1.0     1.0       2.0   
 42348 -0.694952 -0.539572 -1.197666  15816.0    49.0  1.0     2.0       2.0   
 80630 -0.452483 -0.539572 -0.900153  24505.0    49.0  1.0     2.0       2.0   
 
        Cabin_A  Cabin_B  Cabin_C  Cabin_D  Cabin_E  Cabin_F  Cabin_G  Cabin_T  \
 43660        0        0        1        0        0        0        0        0   
 87278        0        0        1        0        0        0        0        0   
 14317        1        0        0        0        0        0        0        0   
 81932        1        0        0        0        0        0        0        0   
 95321        0        0        1        0        0        0        0        0   
 ...        ...      ...      ...      ...      ...      ...      ...      ...   
 42287        0        0        0        0        0        0        0        0   
 4967         0        0        0        0        0        0        0        0   
 47725        0        0        0        0        0        0        0        0   
 42348        0        0        0        0        0        0        0        0   
 80630        0        0        0        0        0        0        0        0   
 
        Cabin_X  
 43660        0  
 87278        0  
 14317        0  
 81932        0  
 95321        0  
 ...        ...  
 42287        1  
 4967         1  
 47725        1  
 42348        1  
 80630        1  
 
 [30001 rows x 17 columns],
 62017    0
 5005     0
 56849    1
 42434    1
 54712    0
         ..
 50057    1
 98047    0
 5192     0
 77708    0
 98539    1
 Name: Survived, Length: 69999, dtype: int64,
 {'parameter': 'Hyperparameters',
  'auc': 'AUC',
  'confusion_matrix': 'Confusion Matrix',
  'threshold': 'Threshold',
  'pr': 'Precision Recall',
  'error': 'Prediction Error',
  'class_report': 'Class Report',
  'rfe': 'Feature Selection',
  'learning': 'Learning Curve',
  'manifold': 'Manifold Learning',
  'calibration': 'Calibration Curve',
  'vc': 'Validation Curve',
  'dimension': 'Dimensions',
  'feature': 'Feature Importance',
  'feature_all': 'Feature Importance (All)',
  'boundary': 'Decision Boundary',
  'lift': 'Lift Chart',
  'gain': 'Gain Chart',
  'tree': 'Decision Tree'},
 None,
 <MLUsecase.CLASSIFICATION: 1>,
 -1,
 43660    1
 87278    0
 14317    0
 81932    1
 95321    0
         ..
 42287    0
 4967     1
 47725    0
 42348    0
 80630    0
 Name: Survived, Length: 30001, dtype: int64,
                 Age     SibSp      Fare     Name  Ticket  Sex  Pclass  \
 0     -8.614253e-16  1.901268  0.134351  17441.0    49.0  1.0     0.0   
 1     -8.614253e-16 -0.539572 -0.533837   3063.0    49.0  1.0     2.0   
 2     -2.069149e+00  0.680848  1.070483  17798.0    14.0  1.0     2.0   
 3     -9.374220e-01 -0.539572 -0.555506  12742.0     0.0  1.0     2.0   
 4     -5.737175e-01 -0.539572 -1.023540   2335.0    49.0  1.0     2.0   
 ...             ...       ...       ...      ...     ...  ...     ...   
 99995  1.669127e+00 -0.539572 -0.434567   1590.0    21.0  0.0     1.0   
 99996  1.911597e+00 -0.539572 -0.698959   2992.0    49.0  1.0     1.0   
 99997  1.536915e-01 -0.539572 -0.802137   4219.0    49.0  1.0     2.0   
 99998  1.002335e+00 -0.539572  0.259408   3941.0    49.0  1.0     2.0   
 99999  1.244805e+00 -0.539572 -0.492531   7055.0    49.0  1.0     2.0   
 
        Embarked  Cabin_A  Cabin_B  Cabin_C  Cabin_D  Cabin_E  Cabin_F  \
 0           2.0        0        0        1        0        0        0   
 1           2.0        0        0        0        0        0        0   
 2           2.0        0        0        0        0        0        0   
 3           2.0        0        0        0        0        0        0   
 4           2.0        0        0        0        0        0        0   
 ...         ...      ...      ...      ...      ...      ...      ...   
 99995       0.0        0        0        0        1        0        0   
 99996       2.0        0        0        0        0        0        0   
 99997       2.0        0        0        0        0        0        0   
 99998       2.0        0        0        0        0        0        0   
 99999       2.0        0        0        0        0        0        0   
 
        Cabin_G  Cabin_T  Cabin_X  
 0            0        0        0  
 1            0        0        1  
 2            0        0        1  
 3            0        0        1  
 4            0        0        1  
 ...        ...      ...      ...  
 99995        0        0        0  
 99996        0        0        1  
 99997        0        0        1  
 99998        0        0        1  
 99999        0        0        1  
 
 [100000 rows x 17 columns],
 5,
 None,
 False)
Description Value 
1
#best_model = compare_models(sort = 'Accuracy', n_select = 4)
1
# gbc=create_model('gbc')
1
# gbc=tune_model(gbc)
1
# print(gbc)
1
2
3
lightgbm=create_model('lightgbm'

                     )
            <tr>
                    <th id="T_222f1_level0_row0" class="row_heading level0 row0" >0</th>
                    <td id="T_222f1_row0_col0" class="data row0 col0" >0.7825</td>
                    <td id="T_222f1_row0_col1" class="data row0 col1" >0.8494</td>
                    <td id="T_222f1_row0_col2" class="data row0 col2" >0.7359</td>
                    <td id="T_222f1_row0_col3" class="data row0 col3" >0.7513</td>
                    <td id="T_222f1_row0_col4" class="data row0 col4" >0.7435</td>
                    <td id="T_222f1_row0_col5" class="data row0 col5" >0.5547</td>
                    <td id="T_222f1_row0_col6" class="data row0 col6" >0.5548</td>
        </tr>
        <tr>
                    <th id="T_222f1_level0_row1" class="row_heading level0 row1" >1</th>
                    <td id="T_222f1_row1_col0" class="data row1 col0" >0.7801</td>
                    <td id="T_222f1_row1_col1" class="data row1 col1" >0.8490</td>
                    <td id="T_222f1_row1_col2" class="data row1 col2" >0.7437</td>
                    <td id="T_222f1_row1_col3" class="data row1 col3" >0.7431</td>
                    <td id="T_222f1_row1_col4" class="data row1 col4" >0.7434</td>
                    <td id="T_222f1_row1_col5" class="data row1 col5" >0.5510</td>
                    <td id="T_222f1_row1_col6" class="data row1 col6" >0.5510</td>
        </tr>
        <tr>
                    <th id="T_222f1_level0_row2" class="row_heading level0 row2" >2</th>
                    <td id="T_222f1_row2_col0" class="data row2 col0" >0.7863</td>
                    <td id="T_222f1_row2_col1" class="data row2 col1" >0.8547</td>
                    <td id="T_222f1_row2_col2" class="data row2 col2" >0.7467</td>
                    <td id="T_222f1_row2_col3" class="data row2 col3" >0.7525</td>
                    <td id="T_222f1_row2_col4" class="data row2 col4" >0.7496</td>
                    <td id="T_222f1_row2_col5" class="data row2 col5" >0.5632</td>
                    <td id="T_222f1_row2_col6" class="data row2 col6" >0.5632</td>
        </tr>
        <tr>
                    <th id="T_222f1_level0_row3" class="row_heading level0 row3" >3</th>
                    <td id="T_222f1_row3_col0" class="data row3 col0" >0.7796</td>
                    <td id="T_222f1_row3_col1" class="data row3 col1" >0.8490</td>
                    <td id="T_222f1_row3_col2" class="data row3 col2" >0.7391</td>
                    <td id="T_222f1_row3_col3" class="data row3 col3" >0.7447</td>
                    <td id="T_222f1_row3_col4" class="data row3 col4" >0.7419</td>
                    <td id="T_222f1_row3_col5" class="data row3 col5" >0.5496</td>
                    <td id="T_222f1_row3_col6" class="data row3 col6" >0.5497</td>
        </tr>
        <tr>
                    <th id="T_222f1_level0_row4" class="row_heading level0 row4" >4</th>
                    <td id="T_222f1_row4_col0" class="data row4 col0" >0.7833</td>
                    <td id="T_222f1_row4_col1" class="data row4 col1" >0.8502</td>
                    <td id="T_222f1_row4_col2" class="data row4 col2" >0.7475</td>
                    <td id="T_222f1_row4_col3" class="data row4 col3" >0.7469</td>
                    <td id="T_222f1_row4_col4" class="data row4 col4" >0.7472</td>
                    <td id="T_222f1_row4_col5" class="data row4 col5" >0.5577</td>
                    <td id="T_222f1_row4_col6" class="data row4 col6" >0.5577</td>
        </tr>
        <tr>
                    <th id="T_222f1_level0_row5" class="row_heading level0 row5" >Mean</th>
                    <td id="T_222f1_row5_col0" class="data row5 col0" >0.7824</td>
                    <td id="T_222f1_row5_col1" class="data row5 col1" >0.8505</td>
                    <td id="T_222f1_row5_col2" class="data row5 col2" >0.7426</td>
                    <td id="T_222f1_row5_col3" class="data row5 col3" >0.7477</td>
                    <td id="T_222f1_row5_col4" class="data row5 col4" >0.7451</td>
                    <td id="T_222f1_row5_col5" class="data row5 col5" >0.5552</td>
                    <td id="T_222f1_row5_col6" class="data row5 col6" >0.5553</td>
        </tr>
        <tr>
                    <th id="T_222f1_level0_row6" class="row_heading level0 row6" >SD</th>
                    <td id="T_222f1_row6_col0" class="data row6 col0" >0.0024</td>
                    <td id="T_222f1_row6_col1" class="data row6 col1" >0.0022</td>
                    <td id="T_222f1_row6_col2" class="data row6 col2" >0.0045</td>
                    <td id="T_222f1_row6_col3" class="data row6 col3" >0.0037</td>
                    <td id="T_222f1_row6_col4" class="data row6 col4" >0.0029</td>
                    <td id="T_222f1_row6_col5" class="data row6 col5" >0.0049</td>
                    <td id="T_222f1_row6_col6" class="data row6 col6" >0.0049</td>
        </tr>
</tbody></table>
Accuracy AUC Recall Prec. F1 Kappa MCC 
1
2
3
lightgbm = tune_model(lightgbm
                     , optimize='AUC' 
                     )
            <tr>
                    <th id="T_9e81c_level0_row0" class="row_heading level0 row0" >0</th>
                    <td id="T_9e81c_row0_col0" class="data row0 col0" >0.7793</td>
                    <td id="T_9e81c_row0_col1" class="data row0 col1" >0.8496</td>
                    <td id="T_9e81c_row0_col2" class="data row0 col2" >0.7382</td>
                    <td id="T_9e81c_row0_col3" class="data row0 col3" >0.7444</td>
                    <td id="T_9e81c_row0_col4" class="data row0 col4" >0.7413</td>
                    <td id="T_9e81c_row0_col5" class="data row0 col5" >0.5488</td>
                    <td id="T_9e81c_row0_col6" class="data row0 col6" >0.5489</td>
        </tr>
        <tr>
                    <th id="T_9e81c_level0_row1" class="row_heading level0 row1" >1</th>
                    <td id="T_9e81c_row1_col0" class="data row1 col0" >0.7797</td>
                    <td id="T_9e81c_row1_col1" class="data row1 col1" >0.8492</td>
                    <td id="T_9e81c_row1_col2" class="data row1 col2" >0.7439</td>
                    <td id="T_9e81c_row1_col3" class="data row1 col3" >0.7424</td>
                    <td id="T_9e81c_row1_col4" class="data row1 col4" >0.7431</td>
                    <td id="T_9e81c_row1_col5" class="data row1 col5" >0.5503</td>
                    <td id="T_9e81c_row1_col6" class="data row1 col6" >0.5503</td>
        </tr>
        <tr>
                    <th id="T_9e81c_level0_row2" class="row_heading level0 row2" >2</th>
                    <td id="T_9e81c_row2_col0" class="data row2 col0" >0.7858</td>
                    <td id="T_9e81c_row2_col1" class="data row2 col1" >0.8547</td>
                    <td id="T_9e81c_row2_col2" class="data row2 col2" >0.7489</td>
                    <td id="T_9e81c_row2_col3" class="data row2 col3" >0.7505</td>
                    <td id="T_9e81c_row2_col4" class="data row2 col4" >0.7497</td>
                    <td id="T_9e81c_row2_col5" class="data row2 col5" >0.5625</td>
                    <td id="T_9e81c_row2_col6" class="data row2 col6" >0.5625</td>
        </tr>
        <tr>
                    <th id="T_9e81c_level0_row3" class="row_heading level0 row3" >3</th>
                    <td id="T_9e81c_row3_col0" class="data row3 col0" >0.7777</td>
                    <td id="T_9e81c_row3_col1" class="data row3 col1" >0.8493</td>
                    <td id="T_9e81c_row3_col2" class="data row3 col2" >0.7467</td>
                    <td id="T_9e81c_row3_col3" class="data row3 col3" >0.7376</td>
                    <td id="T_9e81c_row3_col4" class="data row3 col4" >0.7422</td>
                    <td id="T_9e81c_row3_col5" class="data row3 col5" >0.5468</td>
                    <td id="T_9e81c_row3_col6" class="data row3 col6" >0.5469</td>
        </tr>
        <tr>
                    <th id="T_9e81c_level0_row4" class="row_heading level0 row4" >4</th>
                    <td id="T_9e81c_row4_col0" class="data row4 col0" >0.7825</td>
                    <td id="T_9e81c_row4_col1" class="data row4 col1" >0.8508</td>
                    <td id="T_9e81c_row4_col2" class="data row4 col2" >0.7429</td>
                    <td id="T_9e81c_row4_col3" class="data row4 col3" >0.7477</td>
                    <td id="T_9e81c_row4_col4" class="data row4 col4" >0.7453</td>
                    <td id="T_9e81c_row4_col5" class="data row4 col5" >0.5555</td>
                    <td id="T_9e81c_row4_col6" class="data row4 col6" >0.5555</td>
        </tr>
        <tr>
                    <th id="T_9e81c_level0_row5" class="row_heading level0 row5" >Mean</th>
                    <td id="T_9e81c_row5_col0" class="data row5 col0" >0.7810</td>
                    <td id="T_9e81c_row5_col1" class="data row5 col1" >0.8507</td>
                    <td id="T_9e81c_row5_col2" class="data row5 col2" >0.7441</td>
                    <td id="T_9e81c_row5_col3" class="data row5 col3" >0.7445</td>
                    <td id="T_9e81c_row5_col4" class="data row5 col4" >0.7443</td>
                    <td id="T_9e81c_row5_col5" class="data row5 col5" >0.5528</td>
                    <td id="T_9e81c_row5_col6" class="data row5 col6" >0.5528</td>
        </tr>
        <tr>
                    <th id="T_9e81c_level0_row6" class="row_heading level0 row6" >SD</th>
                    <td id="T_9e81c_row6_col0" class="data row6 col0" >0.0028</td>
                    <td id="T_9e81c_row6_col1" class="data row6 col1" >0.0021</td>
                    <td id="T_9e81c_row6_col2" class="data row6 col2" >0.0036</td>
                    <td id="T_9e81c_row6_col3" class="data row6 col3" >0.0044</td>
                    <td id="T_9e81c_row6_col4" class="data row6 col4" >0.0030</td>
                    <td id="T_9e81c_row6_col5" class="data row6 col5" >0.0056</td>
                    <td id="T_9e81c_row6_col6" class="data row6 col6" >0.0056</td>
        </tr>
</tbody></table>
Accuracy AUC Recall Prec. F1 Kappa MCC 
1
print(lightgbm)
LGBMClassifier(bagging_fraction=0.8, bagging_freq=5, boosting_type='gbdt',
               class_weight=None, colsample_bytree=1.0, feature_fraction=0.9,
               importance_type='split', learning_rate=0.103, max_depth=-1,
               min_child_samples=30, min_child_weight=0.001, min_split_gain=0.4,
               n_estimators=40, n_jobs=-1, num_leaves=30, objective=None,
               random_state=1, reg_alpha=2, reg_lambda=0.2, silent=True,
               subsample=1.0, subsample_for_bin=200000, subsample_freq=0)

1
2
3
catboost=create_model('catboost'

                     )
            <tr>
                    <th id="T_cd7ef_level0_row0" class="row_heading level0 row0" >0</th>
                    <td id="T_cd7ef_row0_col0" class="data row0 col0" >0.7786</td>
                    <td id="T_cd7ef_row0_col1" class="data row0 col1" >0.8480</td>
                    <td id="T_cd7ef_row0_col2" class="data row0 col2" >0.7287</td>
                    <td id="T_cd7ef_row0_col3" class="data row0 col3" >0.7479</td>
                    <td id="T_cd7ef_row0_col4" class="data row0 col4" >0.7382</td>
                    <td id="T_cd7ef_row0_col5" class="data row0 col5" >0.5464</td>
                    <td id="T_cd7ef_row0_col6" class="data row0 col6" >0.5465</td>
        </tr>
        <tr>
                    <th id="T_cd7ef_level0_row1" class="row_heading level0 row1" >1</th>
                    <td id="T_cd7ef_row1_col0" class="data row1 col0" >0.7774</td>
                    <td id="T_cd7ef_row1_col1" class="data row1 col1" >0.8474</td>
                    <td id="T_cd7ef_row1_col2" class="data row1 col2" >0.7394</td>
                    <td id="T_cd7ef_row1_col3" class="data row1 col3" >0.7405</td>
                    <td id="T_cd7ef_row1_col4" class="data row1 col4" >0.7399</td>
                    <td id="T_cd7ef_row1_col5" class="data row1 col5" >0.5453</td>
                    <td id="T_cd7ef_row1_col6" class="data row1 col6" >0.5453</td>
        </tr>
        <tr>
                    <th id="T_cd7ef_level0_row2" class="row_heading level0 row2" >2</th>
                    <td id="T_cd7ef_row2_col0" class="data row2 col0" >0.7826</td>
                    <td id="T_cd7ef_row2_col1" class="data row2 col1" >0.8532</td>
                    <td id="T_cd7ef_row2_col2" class="data row2 col2" >0.7417</td>
                    <td id="T_cd7ef_row2_col3" class="data row2 col3" >0.7486</td>
                    <td id="T_cd7ef_row2_col4" class="data row2 col4" >0.7452</td>
                    <td id="T_cd7ef_row2_col5" class="data row2 col5" >0.5557</td>
                    <td id="T_cd7ef_row2_col6" class="data row2 col6" >0.5557</td>
        </tr>
        <tr>
                    <th id="T_cd7ef_level0_row3" class="row_heading level0 row3" >3</th>
                    <td id="T_cd7ef_row3_col0" class="data row3 col0" >0.7761</td>
                    <td id="T_cd7ef_row3_col1" class="data row3 col1" >0.8477</td>
                    <td id="T_cd7ef_row3_col2" class="data row3 col2" >0.7354</td>
                    <td id="T_cd7ef_row3_col3" class="data row3 col3" >0.7403</td>
                    <td id="T_cd7ef_row3_col4" class="data row3 col4" >0.7379</td>
                    <td id="T_cd7ef_row3_col5" class="data row3 col5" >0.5425</td>
                    <td id="T_cd7ef_row3_col6" class="data row3 col6" >0.5425</td>
        </tr>
        <tr>
                    <th id="T_cd7ef_level0_row4" class="row_heading level0 row4" >4</th>
                    <td id="T_cd7ef_row4_col0" class="data row4 col0" >0.7835</td>
                    <td id="T_cd7ef_row4_col1" class="data row4 col1" >0.8499</td>
                    <td id="T_cd7ef_row4_col2" class="data row4 col2" >0.7410</td>
                    <td id="T_cd7ef_row4_col3" class="data row4 col3" >0.7504</td>
                    <td id="T_cd7ef_row4_col4" class="data row4 col4" >0.7457</td>
                    <td id="T_cd7ef_row4_col5" class="data row4 col5" >0.5572</td>
                    <td id="T_cd7ef_row4_col6" class="data row4 col6" >0.5572</td>
        </tr>
        <tr>
                    <th id="T_cd7ef_level0_row5" class="row_heading level0 row5" >Mean</th>
                    <td id="T_cd7ef_row5_col0" class="data row5 col0" >0.7796</td>
                    <td id="T_cd7ef_row5_col1" class="data row5 col1" >0.8492</td>
                    <td id="T_cd7ef_row5_col2" class="data row5 col2" >0.7373</td>
                    <td id="T_cd7ef_row5_col3" class="data row5 col3" >0.7456</td>
                    <td id="T_cd7ef_row5_col4" class="data row5 col4" >0.7414</td>
                    <td id="T_cd7ef_row5_col5" class="data row5 col5" >0.5494</td>
                    <td id="T_cd7ef_row5_col6" class="data row5 col6" >0.5495</td>
        </tr>
        <tr>
                    <th id="T_cd7ef_level0_row6" class="row_heading level0 row6" >SD</th>
                    <td id="T_cd7ef_row6_col0" class="data row6 col0" >0.0029</td>
                    <td id="T_cd7ef_row6_col1" class="data row6 col1" >0.0022</td>
                    <td id="T_cd7ef_row6_col2" class="data row6 col2" >0.0048</td>
                    <td id="T_cd7ef_row6_col3" class="data row6 col3" >0.0043</td>
                    <td id="T_cd7ef_row6_col4" class="data row6 col4" >0.0034</td>
                    <td id="T_cd7ef_row6_col5" class="data row6 col5" >0.0059</td>
                    <td id="T_cd7ef_row6_col6" class="data row6 col6" >0.0059</td>
        </tr>
</tbody></table>
Accuracy AUC Recall Prec. F1 Kappa MCC 
1
2
3
catboost = tune_model(catboost
                     , optimize='AUC' 
                     )
            <tr>
                    <th id="T_27911_level0_row0" class="row_heading level0 row0" >0</th>
                    <td id="T_27911_row0_col0" class="data row0 col0" >0.7815</td>
                    <td id="T_27911_row0_col1" class="data row0 col1" >0.8501</td>
                    <td id="T_27911_row0_col2" class="data row0 col2" >0.7455</td>
                    <td id="T_27911_row0_col3" class="data row0 col3" >0.7447</td>
                    <td id="T_27911_row0_col4" class="data row0 col4" >0.7451</td>
                    <td id="T_27911_row0_col5" class="data row0 col5" >0.5539</td>
                    <td id="T_27911_row0_col6" class="data row0 col6" >0.5539</td>
        </tr>
        <tr>
                    <th id="T_27911_level0_row1" class="row_heading level0 row1" >1</th>
                    <td id="T_27911_row1_col0" class="data row1 col0" >0.7802</td>
                    <td id="T_27911_row1_col1" class="data row1 col1" >0.8490</td>
                    <td id="T_27911_row1_col2" class="data row1 col2" >0.7485</td>
                    <td id="T_27911_row1_col3" class="data row1 col3" >0.7410</td>
                    <td id="T_27911_row1_col4" class="data row1 col4" >0.7448</td>
                    <td id="T_27911_row1_col5" class="data row1 col5" >0.5518</td>
                    <td id="T_27911_row1_col6" class="data row1 col6" >0.5518</td>
        </tr>
        <tr>
                    <th id="T_27911_level0_row2" class="row_heading level0 row2" >2</th>
                    <td id="T_27911_row2_col0" class="data row2 col0" >0.7861</td>
                    <td id="T_27911_row2_col1" class="data row2 col1" >0.8554</td>
                    <td id="T_27911_row2_col2" class="data row2 col2" >0.7539</td>
                    <td id="T_27911_row2_col3" class="data row2 col3" >0.7486</td>
                    <td id="T_27911_row2_col4" class="data row2 col4" >0.7512</td>
                    <td id="T_27911_row2_col5" class="data row2 col5" >0.5636</td>
                    <td id="T_27911_row2_col6" class="data row2 col6" >0.5636</td>
        </tr>
        <tr>
                    <th id="T_27911_level0_row3" class="row_heading level0 row3" >3</th>
                    <td id="T_27911_row3_col0" class="data row3 col0" >0.7786</td>
                    <td id="T_27911_row3_col1" class="data row3 col1" >0.8494</td>
                    <td id="T_27911_row3_col2" class="data row3 col2" >0.7462</td>
                    <td id="T_27911_row3_col3" class="data row3 col3" >0.7395</td>
                    <td id="T_27911_row3_col4" class="data row3 col4" >0.7428</td>
                    <td id="T_27911_row3_col5" class="data row3 col5" >0.5485</td>
                    <td id="T_27911_row3_col6" class="data row3 col6" >0.5486</td>
        </tr>
        <tr>
                    <th id="T_27911_level0_row4" class="row_heading level0 row4" >4</th>
                    <td id="T_27911_row4_col0" class="data row4 col0" >0.7830</td>
                    <td id="T_27911_row4_col1" class="data row4 col1" >0.8509</td>
                    <td id="T_27911_row4_col2" class="data row4 col2" >0.7482</td>
                    <td id="T_27911_row4_col3" class="data row4 col3" >0.7460</td>
                    <td id="T_27911_row4_col4" class="data row4 col4" >0.7471</td>
                    <td id="T_27911_row4_col5" class="data row4 col5" >0.5570</td>
                    <td id="T_27911_row4_col6" class="data row4 col6" >0.5570</td>
        </tr>
        <tr>
                    <th id="T_27911_level0_row5" class="row_heading level0 row5" >Mean</th>
                    <td id="T_27911_row5_col0" class="data row5 col0" >0.7819</td>
                    <td id="T_27911_row5_col1" class="data row5 col1" >0.8510</td>
                    <td id="T_27911_row5_col2" class="data row5 col2" >0.7485</td>
                    <td id="T_27911_row5_col3" class="data row5 col3" >0.7439</td>
                    <td id="T_27911_row5_col4" class="data row5 col4" >0.7462</td>
                    <td id="T_27911_row5_col5" class="data row5 col5" >0.5550</td>
                    <td id="T_27911_row5_col6" class="data row5 col6" >0.5550</td>
        </tr>
        <tr>
                    <th id="T_27911_level0_row6" class="row_heading level0 row6" >SD</th>
                    <td id="T_27911_row6_col0" class="data row6 col0" >0.0025</td>
                    <td id="T_27911_row6_col1" class="data row6 col1" >0.0023</td>
                    <td id="T_27911_row6_col2" class="data row6 col2" >0.0029</td>
                    <td id="T_27911_row6_col3" class="data row6 col3" >0.0033</td>
                    <td id="T_27911_row6_col4" class="data row6 col4" >0.0028</td>
                    <td id="T_27911_row6_col5" class="data row6 col5" >0.0051</td>
                    <td id="T_27911_row6_col6" class="data row6 col6" >0.0051</td>
        </tr>
</tbody></table>
Accuracy AUC Recall Prec. F1 Kappa MCC 
1
# ada=create_model('ada')
1
# ada=tune_model(ada,optimize='AUC')
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def create_submission(model, test, test_passenger_id, model_name):
    y_pred_test = model.predict_proba(test)[:, 1]
    submission = pd.DataFrame(
        {
            'PassengerId': test_passenger_id, 
            'Survived': (y_pred_test >= 0.5).astype(int),
        }
    )
    submission.to_csv(f"submission_{model_name}.csv", index=False)
    
    return y_pred_test
1
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3
test = all_df.iloc[100000:, :] #100000개~ 
X_test=test.drop(drop_list,axis=1)
X_test.head()

Age SibSp Fare Name Ticket Sex Pclass Embarked Cabin_A Cabin_B Cabin_C Cabin_D Cabin_E Cabin_F Cabin_G Cabin_T Cabin_X
100000 -0.937422 -0.539572 0.949786 10830 49 1 2 2 0 0 0 0 0 0 0 0 1
100001 1.123570 -0.539572 -1.273379 17134 49 0 2 2 0 0 0 0 0 0 0 0 1
100002 -0.937422 -0.539572 0.481059 9978 49 0 0 0 0 1 0 0 0 0 0 0 0
100003 -0.573717 -0.539572 -0.563310 13303 49 1 1 2 0 0 0 0 0 0 0 0 1
100004 -1.058657 -0.539572 0.125497 4406 49 0 0 0 0 1 0 0 0 0 0 0 0 
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test_pred_lightgbm = create_submission(
    lightgbm, X_test, test_df["PassengerId"], "lightgbm"
)
# test_pred_ada = create_submission(
#     ada, X_test, test_df["PassengerId"], "ada"
# )
# test_pred_gbc = create_submission(
#     ada, X_test, test_df["PassengerId"], "gbc"
# )
test_pred_catboost = create_submission(
    catboost, X_test, test_df["PassengerId"], "catboost"
)
1

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test_pred_merged = (

    test_pred_lightgbm + 
    test_pred_catboost
#     test_pred_ada +
#     test_pred_gbc
)

test_pred_merged = np.round(test_pred_merged / 2)

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submission = pd.DataFrame(
    {
        'PassengerId': test_df["PassengerId"], 
        'Survived': test_pred_merged.astype(int),
    }
submission.to_csv(f"submission_merged.csv", index=False)
  File "<ipython-input-39-f1cf7d72db78>", line 6
    submission.to_csv(f"submission_merged.csv", index=False)
             ^
SyntaxError: invalid syntax

1

1

1

Pycaret을 사용한 모델링

https://ggaggu.github.io/2021/04/28/pycaret_modeling/

Author

lhj

Posted on

2021-04-28

Updated on

2021-04-28

Licensed under

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