Sélection de variables par arbres de décision

Réduction de dimension en Python

Jeroen Boeye

Head of Machine Learning, Faktion

Classifieur Random Forest

schéma de random forest

Classifieur Random Forest

from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score

rf = RandomForestClassifier()

rf.fit(X_train, y_train)

print(accuracy_score(y_test, rf.predict(X_test)))

0.99

Classifieur Random Forest

schéma de random forest annoté

Valeurs d'importance des variables

rf = RandomForestClassifier()

rf.fit(X_train, y_train)

print(rf.feature_importances_)

array([0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.04, 0.  , 0.01, 0.01,
       0.  , 0.  , 0.  , 0.  , 0.01, 0.01, 0.  , 0.  , 0.  , 0.  , 0.05,
       ...
       0.  , 0.14, 0.  , 0.  , 0.  , 0.06, 0.  , 0.  , 0.  , 0.  , 0.  ,
       0.  , 0.07, 0.  , 0.  , 0.01, 0.  ])

print(sum(rf.feature_importances_))

1.0

Sélection de variables via l'importance

mask = rf.feature_importances_ > 0.1

print(mask)

array([False, False, ..., True, False])

X_reduced = X.loc[:, mask]

print(X_reduced.columns)

Index(['chestheight', 'neckcircumference', 'neckcircumferencebase',
       'shouldercircumference'], dtype='object')

RFE avec Random Forests

from sklearn.feature_selection import RFE

rfe = RFE(estimator=RandomForestClassifier(), 
          n_features_to_select=6, verbose=1)

rfe.fit(X_train,y_train)

Fitting estimator with 94 features.
Fitting estimator with 93 features
...
Fitting estimator with 8 features.
Fitting estimator with 7 features.

print(accuracy_score(y_test, rfe.predict(X_test))

0.99

RFE avec Random Forests

from sklearn.feature_selection import RFE

rfe = RFE(estimator=RandomForestClassifier(), 
          n_features_to_select=6, step=10, verbose=1)

rfe.fit(X_train,y_train)

Fitting estimator with 94 features.
Fitting estimator with 84 features.
...
Fitting estimator with 24 features.
Fitting estimator with 14 features.

print(X.columns[rfe.support_])

Index(['biacromialbreadth', 'handbreadth', 'handcircumference', 
       'neckcircumference', 'neckcircumferencebase', 'shouldercircumference'], dtype='object')

Passons à la pratique !

Réduction de dimension en Python