sklearn 逻辑回归的特征选择
Feature selection from sklearn logisitc regression
我使用 sklearn 逻辑回归模型为文本创建了一个二元分类模型。现在我想 select 用于模型的特征。我的代码看起来像这样-
train, val, y_train, y_test = train_test_split(np.arange(data.shape[0]), lab, test_size=0.2, random_state=0)
X_train = data[train]
X_test = data[val]
#X_train, X_test, y_train, y_test = train_test_split(data, lab, test_size=0.2)
tfidf_vect = TfidfVectorizer(analyzer='word', ngram_range=(1,3), min_df = 0, stop_words = 'english')
X_tfidf_train = tfidf_vect.fit_transform(X_train)
X_tfidf_test = tfidf_vect.transform(X_test)
clf_lr = LogisticRegression(penalty='l1')
clf_lr.fit(X_tfidf_train, y_train)
feature_names = tfidf_vect.get_feature_names()
print len(feature_names)
y_pred_lr = clf_lr.predict_proba(X_tfidf_test)[:, 1]
执行此操作的最佳方法是什么。
你可以使用sklearn.feature_selection
这里是 link 如何使用它
http://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.RFE.html#sklearn.feature_selection.RFE
我使用 sklearn 逻辑回归模型为文本创建了一个二元分类模型。现在我想 select 用于模型的特征。我的代码看起来像这样-
train, val, y_train, y_test = train_test_split(np.arange(data.shape[0]), lab, test_size=0.2, random_state=0)
X_train = data[train]
X_test = data[val]
#X_train, X_test, y_train, y_test = train_test_split(data, lab, test_size=0.2)
tfidf_vect = TfidfVectorizer(analyzer='word', ngram_range=(1,3), min_df = 0, stop_words = 'english')
X_tfidf_train = tfidf_vect.fit_transform(X_train)
X_tfidf_test = tfidf_vect.transform(X_test)
clf_lr = LogisticRegression(penalty='l1')
clf_lr.fit(X_tfidf_train, y_train)
feature_names = tfidf_vect.get_feature_names()
print len(feature_names)
y_pred_lr = clf_lr.predict_proba(X_tfidf_test)[:, 1]
执行此操作的最佳方法是什么。
你可以使用sklearn.feature_selection
这里是 link 如何使用它 http://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.RFE.html#sklearn.feature_selection.RFE