mlearner version: 0.2.0
FeatureSelection
FeatureSelection(random_state=99)
None
Methods
LightGBM(X, y, n_estimators=100)
LightGBM
Normalization: No Impute missing values: No
RandomForest(X, y, n_estimators=100)
Random Forest
Normalization: No Impute missing values: Yes
Summary(X, y, k='all', cor_pearson=True, chi2=True, wrapper=True, embeded=True, RandomForest=True, LightGBM=True)
Resumen de la seleccion de caracteristicas.
chi2(X, y, k='all')
Chi-2 Normalization: MinMaxScaler (values should be bigger than 0) Impute missing values: yes
cor_pearson(X, y, k='all')
Pearson Correlation Normalization: no Impute missing values: yes
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
transform_data(X, selector, features)
Transformar el conjunto de entrenamiento al nuevo esquema proporcionado por el selector
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