Python特征分析重要性的常用方法

本文主要是介绍Python特征分析重要性的常用方法，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

前言

特征重要性分析用于了解每个特征(变量或输入)对于做出预测的有用性或价值。目标是确定对模型输出影响最大的最重要的特征，它是机器学习中经常使用的一种方法。

为什么特征重要性分析很重要?

如果有一个包含数十个甚至数百个特征的数据集，每个特征都可能对你的机器学习模型的性能有所贡献。但是并不是所有的特征都是一样的。有些可能是冗余的或不相关的，这会增加建模的复杂性并可能导致过拟合。

特征重要性分析可以识别并关注最具信息量的特征，从而带来以下几个优势:

改进的模型性能
减少过度拟合
更快的训练和推理
增强的可解释性

下面我们深入了解在Python中的一些特性重要性分析的方法。

特征重要性分析方法

1、排列重要性 PermutationImportance

该方法会随机排列每个特征的值，然后监控模型性能下降的程度。如果获得了更大的下降意味着特征更重要

 from sklearn.datasets import load_breast_cancerfrom sklearn.ensemble import RandomForestClassifierfrom sklearn.inspection import permutation_importancefrom sklearn.model_selection import train_test_splitimport matplotlib.pyplot as pltcancer = load_breast_cancer()X_train, X_test, y_train, y_test = train_test_split(cancer.data, cancer.target, random_state=1)rf = RandomForestClassifier(n_estimators=100, random_state=1)rf.fit(X_train, y_train)baseline = rf.score(X_test, y_test)result = permutation_importance(rf, X_test, y_test, n_repeats=10, random_state=1, scoring='accuracy')importances = result.importances_mean# Visualize permutation importancesplt.bar(range(len(importances)), importances)plt.xlabel('Feature Index')plt.ylabel('Permutation Importance')plt.show()

2、内置特征重要性(coef_或feature_importances_)

一些模型，如线性回归和随机森林，可以直接输出特征重要性分数。这些显示了每个特征对最终预测的贡献。

 from sklearn.datasets import load_breast_cancerfrom sklearn.ensemble import RandomForestClassifierX, y = load_breast_cancer(return_X_y=True)rf = RandomForestClassifier(n_estimators=100, random_state=1)rf.fit(X, y)importances = rf.feature_importances_# Plot importancesplt.bar(range(X.shape[1]), importances)plt.xlabel('Feature Index')plt.ylabel('Feature Importance')plt.show()

3、Leave-one-out

迭代地每次删除一个特征并评估准确性。

 from sklearn.datasets import load_breast_cancerfrom sklearn.model_selection import train_test_splitfrom sklearn.ensemble import RandomForestClassifierfrom sklearn.metrics import accuracy_scoreimport matplotlib.pyplot as pltimport numpy as np# Load sample dataX, y = load_breast_cancer(return_X_y=True)# Split data into train and test setsX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1)# Train a random forest modelrf = RandomForestClassifier(n_estimators=100, random_state=1)rf.fit(X_train, y_train)# Get baseline accuracy on test database_acc = accuracy_score(y_test, rf.predict(X_test))# Initialize empty list to store importancesimportances = []# Iterate over all columns and remove one at a timefor i in range(X_train.shape[1]):X_temp = np.delete(X_train, i, axis=1)rf.fit(X_temp, y_train)acc = accuracy_score(y_test, rf.predict(np.delete(X_test, i, axis=1)))importances.append(base_acc - acc)# Plot importance scores    plt.bar(range(len(importances)), importances)plt.show()

4、相关性分析

计算各特征与目标变量之间的相关性。相关性越高的特征越重要。

 import pandas as pdfrom sklearn.datasets import load_breast_cancerX, y = load_breast_cancer(return_X_y=True)df = pd.DataFrame(X, columns=range(30))df['y'] = ycorrelations = df.corrwith(df.y).abs()correlations.sort_values(ascending=False, inplace=True)correlations.plot.bar()

5、递归特征消除 Recursive Feature Elimination

递归地删除特征并查看它如何影响模型性能。删除时会导致更大下降的特征更重要。

from sklearn.ensemble import RandomForestClassifierfrom sklearn.feature_selection import RFEimport pandas as pdfrom sklearn.datasets import load_breast_cancerimport matplotlib.pyplot as pltX, y = load_breast_cancer(return_X_y=True)df = pd.DataFrame(X, columns=range(30))df['y'] = yrf = RandomForestClassifier()rfe = RFE(rf, n_features_to_select=10)rfe.fit(X, y)print(rfe.ranking_)

输出为[6 4 11 12 7 11 18 21 8 16 10 3 15 14 19 17 20 13 11 11 12 9 11 5 11]

6、XGBoost特性重要性

计算一个特性用于跨所有树拆分数据的次数。更多的分裂意味着更重要。

 import xgboost as xgbimport pandas as pdfrom sklearn.datasets import load_breast_cancerimport matplotlib.pyplot as pltX, y = load_breast_cancer(return_X_y=True)df = pd.DataFrame(X, columns=range(30))df['y'] = ymodel = xgb.XGBClassifier()model.fit(X, y)importances = model.feature_importances_importances = pd.Series(importances, index=range(X.shape[1]))importances.plot.bar()

7、主成分分析 PCA

对特征进行主成分分析，并查看每个主成分的解释方差比。在前几个组件上具有较高负载的特性更为重要。

 from sklearn.decomposition import PCAimport pandas as pdfrom sklearn.datasets import load_breast_cancerimport matplotlib.pyplot as pltX, y = load_breast_cancer(return_X_y=True)df = pd.DataFrame(X, columns=range(30))df['y'] = ypca = PCA()pca.fit(X)plt.bar(range(pca.n_components_), pca.explained_variance_ratio_)plt.xlabel('PCA components')plt.ylabel('Explained Variance')

8、方差分析 ANOVA

使用f_classif()获得每个特征的方差分析f值。f值越高，表明特征与目标的相关性越强。

 from sklearn.feature_selection import f_classifimport pandas as pdfrom sklearn.datasets import load_breast_cancerimport matplotlib.pyplot as pltX, y = load_breast_cancer(return_X_y=True)df = pd.DataFrame(X, columns=range(30))df['y'] = yfval = f_classif(X, y)fval = pd.Series(fval[0], index=range(X.shape[1]))fval.plot.bar()

9、卡方检验

使用chi2()获得每个特征的卡方统计信息。得分越高的特征越有可能独立于目标。

from sklearn.feature_selection import chi2import pandas as pdfrom sklearn.datasets import load_breast_cancerimport matplotlib.pyplot as pltX, y = load_breast_cancer(return_X_y=True)df = pd.DataFrame(X, columns=range(30))df['y'] = ychi_scores = chi2(X, y)chi_scores = pd.Series(chi_scores[0], index=range(X.shape[1]))chi_scores.plot.bar()