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Clasificación de funciones con eliminación de funciones recursivas en Scikit-Learn

Anticipándonos al inicio del curso "Machine Learning. Professional", publicamos una traducción de un artículo útil.



También lo invitamos a ver la grabación del seminario web abierto sobre el tema "Clustering" .

– . , , , . . . .

Recursive Feature Elimination

, (recursive feature elimination), , , . 

. . . , .

Sklearn

Scikit-learn sklearn.featureselection.RFE. :

  • estimator – , coef featureimportances attributes.

  • nfeaturestoselect – . .

  • step – , , , 0 1, , .

:

  • ranking — .

  • nfeatures — .

  • support — , , .

, , featureimportances coeff. . 13 . .

import pandas as pddf = pd.read_csv(‘heart.csv’)df.head()

x y.

X = df.drop([‘target’],axis=1)
y = df[‘target’]

:

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y,random_state=0)

:

  • Pipeline – -, .

  • RepeatedStratifiedKFold – k- -. 

  • crossvalscore – -.

  • GradientBoostingClassifier – , .

  • Numpy – .

from sklearn.pipeline import Pipeline
from sklearn.model_selection import RepeatedStratifiedKFold
from sklearn.model_selection import cross_val_score
from sklearn.feature_selection import RFE
import numpy as np
from sklearn.ensemble import GradientBoostingClassifier

RFE , . 6:

rfe = RFE(estimator=GradientBoostingClassifier(), n_features_to_select=6)

, :

model = GradientBoostingClassifier()

Pipeline . Pipeline rfe , . 

RepeatedStratifiedKFold 10 5 . k- - , . RepeatedStratifiedKFold k- - . 

pipe = Pipeline([(‘Feature Selection’, rfe), (‘Model’, model)])
cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=5, random_state=36851234)
n_scores = cross_val_score(pipe, X_train, y_train, scoring=’accuracy’, cv=cv, n_jobs=-1)
np.mean(n_scores)

— .

pipe.fit(X_train, y_train)

support . Support . 

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

.

pd.DataFrame(rfe.support_,index=X.columns,columns=[‘Rank’])

.

rf_df = pd.DataFrame(rfe.ranking_,index=X.columns,columns=[‘Rank’]).sort_values(by=’Rank’,ascending=True)rf_df.head()

, , , . -. sklearn.featureselection.RFECV. :

  • estimator – RFE.

  • minfeaturestoselect — .

  • cv — -.

:

  • nfeatures — , -.

  • support — , .

  • ranking — .

  • gridscores — , -.

. 

from sklearn.feature_selection import RFECVrfecv = RFECV(estimator=GradientBoostingClassifier())

cv. rfecv.

pipeline = Pipeline([(‘Feature Selection’, rfecv), (‘Model’, model)])
cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=5, random_state=36851234)
n_scores = cross_val_score(pipeline, X_train, y_train, scoring=’accuracy’, cv=cv, n_jobs=-1)
np.mean(n_scores)

.

pipeline.fit(X_train,y_train)

nfeatures.

print(“Optimal number of features : %d” % rfecv.n_features_)Optimal number of features : 7

support , .

rfecv.support_rfecv_df = pd.DataFrame(rfecv.ranking_,index=X.columns,columns=[‘Rank’]).sort_values(by=’Rank’,ascending=True)
rfecv_df.head()

gridscores , -.

import matplotlib.pyplot as plt
plt.figure(figsize=(12,6))
plt.xlabel(“Number of features selected”)
plt.ylabel(“Cross validation score (nb of correct classifications)”)
plt.plot(range(1, len(rfecv.grid_scores_) + 1), rfecv.grid_scores_)
plt.show()

. . , , .

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