Random PCA Explainer Using PermutationImportance

This function first applies PCA (Principal Component Analysis) to reduce the dimensions of the dataset, and then uses PermutationImportance to explain the importance of the principal components. PermutationImportance evaluates the importance of features by randomly shuffling feature values and observing the change in model performance.

Technology Stack : numpy, sklearn.decomposition.PCA, eli5.sklearn.PermutationImportance

Code Type : The type of code

Code Difficulty : Intermediate

                
                    
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 a dataset and uses PermutationImportance to explain the importance of the features.
    """
    pca = PCA(n_components=n_components)
    X_pca = pca.fit_transform(X)
    
    # PermutationImportance to explain the importance of the components
    permutation_importance = PermutationImportance(pca).fit(X_pca)
    
    return permutation_importance.importances_mean_