FeatureSelection

FeatureSelection(random_state=99)

None

Methods

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LightGBM(X, y)

LightGBM

Normalization: No Impute missing values: No

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RandomForest(X, y, n_estimators=100)

Random Forest

Normalization: No Impute missing values: Yes

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Summary(X, y, cor_pearson=True, chi2=True, wrapper=True, embeded=True, RandomForest=True, LightGBM=True)

Resumen de la seleccion de caracteristicas.

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chi2(X, y, k='all')

Chi-2 Normalization: MinMaxScaler (values should be bigger than 0) Impute missing values: yes

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cor_pearson(X, y, k='all')

Pearson Correlation Normalization: no Impute missing values: yes

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embeded(X, y)

Embeded documentation for SelectFromModel: http://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.SelectFromModel.html ### 3.1 Logistics Regression L1 Note Normalization: Yes Impute missing values: Yes

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transform_data(X, selector, features)

Transformar el conjunto de entrenamiento al nuevo esquema proporcionado por el selector

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wrapper(X, y, k='all')

Wrapper documentation for RFE: http://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.RFE.html

Normalization: depend on the used model; yes for LR Impute missing values: depend on the used model; yes for LR