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 import numpy as np 

import matplotlib.pyplot as plt 

from sklearn.datasets import fetch_olivetti_faces 

from sklearn.model_selection import train_test_split, cross_val_score 

from sklearn.naive_bayes import GaussianNB 

from sklearn.metrics import accuracy_score, classification_report, 

confusion_matrix 

# Load dataset 

data = fetch_olivetti_faces(shuffle=True, random_state=42) 

X, y = data.data, data.target 

# Train-test split 

X_train, X_test, y_train, y_test = train_test_split(X, y, 

test_size=0.3, random_state=42) 

# Train Naive Bayes model 

model = GaussianNB() 

model.fit(X_train, y_train) 

y_pred = model.predict(X_test) 

# Evaluation 

print(f'Accuracy: {accuracy_score(y_test, y_pred) * 100:.2f}%') 

print("\nClassification Report:") 

print(classification_report(y_test, y_pred, zero_division=0)) 

print("\nConfusion Matrix:") 

print(confusion_matrix(y_test, y_pred)) 

# Cross-validation 

cv_score = cross_val_score(model, X, y, cv=5).mean() 

print(f'\nCross-validation Accuracy: {cv_score * 100:.2f}%') 

# Show some predictions 

fig, axes = plt.subplots(3, 5, figsize=(12, 8)) 

for ax, img, true, pred in zip(axes.ravel(), X_test, y_test, y_pred): 

ax.imshow(img.reshape(64, 64), cmap='gray') 

ax.set_title(f"True: {true}, Pred: {pred}") 

ax.axis('off') 

plt.tight_layout() 

plt.show()

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