来源：Deephub Imba 
  
 
 
   
 

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。

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• 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

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from sklearn.datasets import load_breast_cancer from sklearn.ensemble import RandomForestClassifier from sklearn.inspection import permutation_importance from sklearn.model_selection import train_test_split import matplotlib.pyplot as plt
 cancer = 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 importances plt.bar(range(len(importances)), importances) plt.xlabel(‘Feature Index’) plt.ylabel(‘Permutation Importance’) plt.show()
 
 
   
 
 
 
   
 
 
  
    
  
 
 
   
 

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

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 from sklearn.datasets import load_breast_cancer from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import accuracy_score import matplotlib.pyplot as plt import numpy as np
 # Load sample data X, y = load_breast_cancer(return_X_y=True)
 # Split data into train and test sets X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1)
 # Train a random forest model rf = RandomForestClassifier(n_estimators=100, random_state=1) rf.fit(X_train, y_train)
 # Get baseline accuracy on test data base_acc = accuracy_score(y_test, rf.predict(X_test))
 # Initialize empty list to store importances importances = []
 # Iterate over all columns and remove one at a time for 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()
 
 
   
 
 
 
   
 
 
  
    
  
 
 
   
 

讯享网 import pandas as pd from sklearn.datasets import load_breast_cancer
 X, y = load_breast_cancer(return_X_y=True) df = pd.DataFrame(X, columns=range(30)) df[‘y’] = y
 correlations = df.corrwith(df.y).abs() correlations.sort_values(ascending=False, inplace=True)
 correlations.plot.bar()

 from sklearn.ensemble import RandomForestClassifier from sklearn.feature_selection import RFE import pandas as pd from sklearn.datasets import load_breast_cancer import matplotlib.pyplot as plt
 X, y = load_breast_cancer(return_X_y=True) df = pd.DataFrame(X, columns=range(30)) df[‘y’] = y
 rf = 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]

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讯享网 import xgboost as xgb import pandas as pd from sklearn.datasets import load_breast_cancer import matplotlib.pyplot as plt
 X, y = load_breast_cancer(return_X_y=True) df = pd.DataFrame(X, columns=range(30)) df[‘y’] = y
 model = xgb.XGBClassifier() model.fit(X, y)
 importances = model.featureimportances importances = pd.Series(importances, index=range(X.shape[1])) importances.plot.bar()
 
 
   
 

 
 
   
 
 
  
    
  
 
   
 
   
 
   
 
   
 
   
 
   
 
   
 
   
 
   
 
   
 
   
 
   
 
   
 
   
 
   
 
  
 
  
    
  
讯享网 from sklearn.decomposition import PCA import pandas as pd from sklearn.datasets import load_breast_cancer import matplotlib.pyplot as plt
 X, y = load_breast_cancer(return_X_y=True) df = pd.DataFrame(X, columns=range(30)) df[‘y’] = y
 pca = PCA() pca.fit(X)
 plt.bar(range(pca.ncomponents), pca.explained_varianceratio) plt.xlabel(‘PCA components’) plt.ylabel(‘Explained Variance’)
 
 
   
 

 
 
   
 
 
  
    
  
 
   
 
   
 
   
 
   
 
   
 
   
 
   
 
   
 
   
 
   
 
   
 
   
 
  
 
  
    
  
讯享网 from sklearn.feature_selection import f_classif import pandas as pd from sklearn.datasets import load_breast_cancer import matplotlib.pyplot as plt
 X, y = load_breast_cancer(return_X_y=True) df = pd.DataFrame(X, columns=range(30)) df[‘y’] = y
 fval = f_classif(X, y) fval = pd.Series(fval[0], index=range(X.shape[1])) fval.plot.bar()
 
 
   
 

 
 
   
 
 
  
    
  
 
   
 
   
 
   
 
   
 
   
 
   
 
   
 
   
 
   
 
   
 
   
 
   
 
  
 
  
    
  
讯享网 from sklearn.feature_selection import chi2 import pandas as pd from sklearn.datasets import load_breast_cancer import matplotlib.pyplot as plt
 X, y = load_breast_cancer(return_X_y=True) df = pd.DataFrame(X, columns=range(30)) df[‘y’] = y
 chi_scores = chi2(X, y) chi_scores = pd.Series(chi_scores[0], index=range(X.shape[1])) chi_scores.plot.bar()
 
 
   
 

• 尝试多种方法以获得更健壮的视图；
• 聚合结果的集成方法；
• 更多地关注相对顺序，而不是绝对值；
• 差异并不一定意味着有问题，检查差异的原因会对数据和模型有更深入的了解。