Feature importance in clustering with ARI
Leverage the Adjusted Rand Index (ARI) to quantitatively measure the impact of each feature's removal on cluster assignments in the customer dataset you've worked with in the previous exercise, pre-loaded in X.
The adjusted_rand_score() function and the column_names variable have been pre-loaded for you.
Questo esercizio fa parte del corso
Explainable AI in Python
Istruzioni dell'esercizio
- Derive the original cluster assignments in
original_clusters. - In the for loop, remove features one by one and save the result in
X_reduced. - Derive the
reduced_clustersby applying K-means onX_reduced. - Compute the feature
importancebased on ARI between thereduced_clustersand theoriginal_clusters.
Esercizio pratico interattivo
Prova a risolvere questo esercizio completando il codice di esempio.
kmeans = KMeans(n_clusters=5, random_state=10, n_init=10).fit(X)
# Derive original clusters
original_clusters = ____
for i in range(X.shape[1]):
# Remove feature at index i
X_reduced = ____
# Derive reduced clusters
reduced_clusters = ____
# Derive feature importance
importance = ____
print(f'{column_names[i]}: {importance}')