如何使用权重在逻辑回归中获得特征重要性？

Question

我有一个评论数据集，其类别标签为正面/负面。我正在对该评论数据集应用逻辑回归。首先，我正在转换成词袋。这里sorted_data['Text']是评论，final_counts是一个稀疏矩阵

count_vect = CountVectorizer() 
final_counts = count_vect.fit_transform(sorted_data['Text'].values)
standardized_data = StandardScaler(with_mean=False).fit_transform(final_counts)

将数据集拆分为训练和测试

X_1, X_test, y_1, y_test = cross_validation.train_test_split(final_counts, labels, test_size=0.3, random_state=0)
X_tr, X_cv, y_tr, y_cv = cross_validation.train_test_split(X_1, y_1, test_size=0.3)

我正在应用逻辑回归算法如下

optimal_lambda = 0.001000
log_reg_optimal = LogisticRegression(C=optimal_lambda)

# fitting the model
log_reg_optimal.fit(X_tr, y_tr)

# predict the response
pred = log_reg_optimal.predict(X_test)

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

我的体重是

weights = log_reg_optimal.coef_ .   #<class 'numpy.ndarray'>

array([[-0.23729528, -0.16050616, -0.1382504 , ...,  0.27291847,
         0.35857267,  0.41756443]])
(1, 38178) #shape of weights

我想获得特征重要性，即；前 100 个具有高权重的特征。谁能告诉我如何获得它们？

Answer 1

调查“的一种方式影响”或“重要的线性分类模型给定的特征/参数”是考虑大小的的系数。

这是最基本的做法。其他用于查找特征重要性或参数影响的技术可以提供更多洞察，例如使用p 值、引导分数、各种“判别指数”等。

在这里，你已经标准化了数据，所以直接使用这个：

weights = log_reg_optimal.coef_
abs_weights = np.abs(weights)

print(abs_weights)

如果您查看原始数据，weights那么负系数意味着相应特征的较高值会将分类推向负类。

编辑 1

显示如何获取特征名称的示例：

import numpy as np

#features names
names_of_variables =np.array(['a','b','c','d'])

#create random weights and get the magnitude
weights = np.random.rand(4)
abs_weights = np.abs(weights)

#get the sorting indices
sorted_index = np.argsort(abs_weights)[::-1]

#check if the sorting indices are correct
print(abs_weights[sorted_index])

#get the index of the top-2 features
top_2 = sorted_index[:2]

#get the names of the top 2 most important features
print(names_of_variables[top_2])