Nine commonly used Python feature importance analysis methods

Feature importance analysis is used to understand the usefulness or value of each feature (variable or input) in making predictions. The goal is to identify the most important features that have the greatest impact on the model output, and it is a method often used in machine learning.

Why is feature importance analysis important?

If there is a feature containing dozens or even numbers A dataset of hundreds of features, each of which may contribute to the performance of your machine learning model. But not all features are created equal. Some may be redundant or irrelevant, which increases modeling complexity and may lead to overfitting.

Feature importance analysis can identify and focus on the most informative features, resulting in several advantages: 1. Provide insights: By analyzing the importance of features, we can gain insights into which features in the data have the greatest impact on the results, thus helping us better understand the nature of the data. 2. Optimize the model: By identifying key features, we can optimize the performance of the model, reduce unnecessary computing and storage overhead, and improve the training and prediction efficiency of the model. 3. Feature selection: Feature importance analysis can help us select the features with the most predictive power, thereby improving the accuracy and generalization ability of the model. 4. Explain the model: Feature importance analysis can also help us explain the prediction results of the model, reveal the patterns and causal relationships behind the model, and enhance the interpretability of the model

• Improvement Model performance
• Reduced overfitting
• Faster training and inference
• Enhanced Interpretability

# Let’s take a deeper look at some methods of feature importance analysis in Python.

Feature Importance Analysis Method

1. Permutation ImportancePermutationImportance

This method The values ​​of each feature are randomly arranged, and then the degree of model performance degradation is monitored. If the decrease is larger, it means that the feature is more important

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()

2. Built-in feature importance (coef_ or feature_importances_)

Some models, such as linear regression and random forests, can directly output feature importance scores. These show the contribution of each feature to the final prediction.

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.feature_importances_  # Plot importances plt.bar(range(X.shape[1]), importances) plt.xlabel('Feature Index')  plt.ylabel('Feature Importance') plt.show()

3. Leave-one-out

Iteratively delete one feature at a time and evaluate accuracy.

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()

4. Correlation analysis

The content that needs to be rewritten is: Calculation features and goals Correlation between variables, the higher the correlation, the more important the feature

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()

##5. Recursive Feature Elimination

Recursively remove features and see how it affects model performance. Features that result in larger drops when removed are more important.

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_)

The output is [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 feature importance

Calculate the number of times a feature is used in splitting data. This feature is used in all trees. More splits mean more important

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.feature_importances_ importances = pd.Series(importances, index=range(X.shape[1]))  importances.plot.bar()

7. Principal component analysis PCA

pair Perform principal component analysis on the features and view the explained variance ratio of each principal component. Characteristics with higher loads on the first few components are more important.

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.n_components_), pca.explained_variance_ratio_)  plt.xlabel('PCA components') plt.ylabel('Explained Variance')

8. Analysis of variance ANOVA

Use f_classif() to obtain the analysis of variance of each feature f value. The higher the f value, the stronger the correlation between the feature and the target.

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()

9. Chi-square test

Use the chi2() function to obtain the value of each feature Chi-square statistics. Features with higher scores are more likely to be independent of the target variable

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()

为什么不同的方法会检测到不同的特征?

由于不同的特征重要性方法，有时可以确定哪些特征是最重要的

1、他们用不同的方式衡量重要性:

有的使用不同特特征进行预测，监控精度下降

像XGBOOST或者回归模型使用内置重要性来进行特征的重要性排序

而PCA着眼于方差解释

2、不同模型有不同模型的方法：

线性模型偏向于处理线性关系，而树模型则更倾向于捕捉接近根节点的特征

3、交互作用:

有些方法可以获取特征之间的相互关系，而有些方法则不行，这会导致结果的不同

3、不稳定:

使用不同的数据子集，重要性值可能在同一方法的不同运行中有所不同，这是因为数据差异决定的

4、Hyperparameters:

通过调整超参数，例如主成分分析（PCA）组件或决策树的深度，也会对结果产生影响

所以不同的假设、偏差、数据处理和方法的可变性意味着它们并不总是在最重要的特征上保持一致。

选择特征重要性分析方法的一些最佳实践

• 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

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