PCA-Based Feature Importance Explainer

This function first applies PCA (Principal Component Analysis) to the input data and then uses PermutationImportance from the eli5 library to assess the importance of each feature after the PCA transformation.

Technology Stack : Python, NumPy, scikit-learn, eli5

Code Type : Python Function

Code Difficulty : Intermediate

                
                    
import random
import numpy as np
from sklearn.decomposition import PCA
from eli5.sklearn import PermutationImportance

def random_pca_explainer(X, n_components=2):
    """
    This function applies PCA on the input data and then uses PermutationImportance
    from the eli5 library to explain the importance of each feature after PCA transformation.
    """
    pca = PCA(n_components=n_components)
    X_pca = pca.fit_transform(X)
    
    perm_importance = PermutationImportance(pca).fit(X_pca)
    
    return perm_importance.importances_mean_