如何获得决策树中的特征重要性？

Question

我有一个评论数据集，其 class 标签为 positive/negative。我正在将决策树应用于该评论数据集。首先，我正在转换成一个词袋。这里 sorted_data['Text'] 是评论，final_counts 是稀疏矩阵。

我正在将数据拆分为训练和测试数据集。

X_tr, X_test, y_tr, y_test = cross_validation.train_test_split(sorted_data['Text'], labels, test_size=0.3, random_state=0)

# BOW
count_vect = CountVectorizer() 
count_vect.fit(X_tr.values)
final_counts = count_vect.transfrom(X_tr.values)

按如下方式应用决策树算法

# instantiate learning model k = optimal_k
# Applying the vectors of train data on the test data
optimal_lambda = 15
final_counts_x_test = count_vect.transform(X_test.values)
bow_reg_optimal = DecisionTreeClassifier(max_depth=optimal_lambda,random_state=0)

# fitting the model
bow_reg_optimal.fit(final_counts, y_tr)

# predict the response
pred = bow_reg_optimal.predict(final_counts_x_test)

# evaluate accuracy
acc = accuracy_score(y_test, pred) * 100
print('\nThe accuracy of the Decision Tree for depth = %f is %f%%' % (optimal_lambda, acc))

bow_reg_optimal 是决策树 classifier。谁能告诉我如何使用决策树 classifier 获得 特征重要性 ？

Answer 1

使用 feature_importances_ 属性，一旦调用 fit() 就会定义该属性。例如：

import numpy as np
X = np.random.rand(1000,2)
y = np.random.randint(0, 5, 1000)

from sklearn.tree import DecisionTreeClassifier

tree = DecisionTreeClassifier().fit(X, y)
tree.feature_importances_
# array([ 0.51390759,  0.48609241])

如何获得决策树中的特征重要性？

How to get feature importance in Decision Tree?

python

machine-learning

decision-tree

scikit-learn

sklearn-pandas