AutoVIML: Automatisiertes maschinelles Lernen

Die Übersetzung des Artikels wurde speziell für Studenten des Kurses "Industrial ML on Big Data" erstellt.

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AutoVIML

python-, autoviml pip.

pip install autoviml

AutoVIML

from autoviml.Auto_ViML import Auto_ViML

AutoVIML . , Kaggle. . 

import pandas as pd
df = pd.read_csv('heart_d.csv')
df

, autoviml , AutoVIML.

#Basic Example with all parameters
model, features, trainm, testm = Auto_ViML(
    train,
    target,
    test,
    sample_submission,
    hyper_param="GS",
    feature_reduction=True,
    scoring_parameter="weighted-f1",
    KMeans_Featurizer=False,
    Boosting_Flag=False,
    Binning_Flag=False,
    Add_Poly=False,
    Stacking_Flag=False,
    Imbalanced_Flag=False,
    verbose=0,
)

, AutoVIML, . , .

1. train: , dataframe, dataframe. dataframe «df», «df».

2. target: . «TenYearCHD». 

3. test: . ( ””), , AutoVIML .

4. sample_submission: , .

5. hyper_param: RandomizedSearchCV, , Grid Search CV. «RS».

6. feature_reduction: true, .

7. scoring_parameter: , . «Weighted-f1».

8. KMeans_featurizer: true false XGboost , .

9. boosting_flag: . false.

10. binning_flag: false, true, .

11. add_poly: false.

12. stacking_flag: false. true, , . false.

13. Imbalanced_flag: true, SMOTING.

14. Verbose: . 3.

AutoVIML.

model, features, trainm, testm = Auto_ViML(
    train=df,
    target="TenYearCHD",
    test="",
    sample_submission="",
    hyper_param="RS",
    feature_reduction=True,
    scoring_parameter="weighted-f1",
    KMeans_Featurizer=False,
    Boosting_Flag=True,
    Binning_Flag=False,
    Add_Poly=False,
    Stacking_Flag=True,
    Imbalanced_Flag=True,
    verbose=3
)

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