9. Develop a program to implement the Naive Bayesian classifier considering Olivetti Face Data set for training. Compute the accuracy of the classifier, considering a few test data sets.
PROGRAM:
import numpy as np
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
import matplotlib.pyplot as plt
data = fetch_olivetti_faces(shuffle=True, random_state=42)
X = data.data
y = data.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
gnb = GaussianNB()
gnb.fit(X_train, y_train)
y_pred = gnb.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print(f'Accuracy: {accuracy * 100:.2f}%')
print("\nClassification Report:")
print(classification_report(y_test, y_pred, zero_division=1))
print("\nConfusion Matrix:")
print(confusion_matrix(y_test, y_pred))
cross_val_accuracy = cross_val_score(gnb, X, y, cv=5, scoring='accuracy')
print(f'\nCross-validation accuracy: {cross_val_accuracy.mean() * 100:.2f}%')
fig, axes = plt.subplots(3, 5, figsize=(12, 8))
for ax, image, label, prediction in zip(axes.ravel(), X_test, y_test, y_pred):
ax.imshow(image.reshape(64, 64), cmap=plt.cm.gray)
ax.set_title(f"True: {label}, Pred: {prediction}")
ax.axis('off')
plt.show()OUTPUT:
Accuracy: 80.83%
Classification Report:
precision recall f1-score support
0 0.67 1.00 0.80 2
1 1.00 1.00 1.00 2
2 0.33 0.67 0.44 3
3 1.00 0.00 0.00 5
4 1.00 0.50 0.67 4
5 1.00 1.00 1.00 2
7 1.00 0.75 0.86 4
8 1.00 0.67 0.80 3
9 1.00 0.75 0.86 4
10 1.00 1.00 1.00 3
11 1.00 1.00 1.00 1
12 0.40 1.00 0.57 4
13 1.00 0.80 0.89 5
14 1.00 0.40 0.57 5
15 0.67 1.00 0.80 2
16 1.00 0.67 0.80 3
17 1.00 1.00 1.00 3
18 1.00 1.00 1.00 3
19 0.67 1.00 0.80 2
20 1.00 1.00 1.00 3
21 1.00 0.67 0.80 3
22 1.00 0.60 0.75 5
23 1.00 0.75 0.86 4
24 1.00 1.00 1.00 3
25 1.00 0.75 0.86 4
26 1.00 1.00 1.00 2
27 1.00 1.00 1.00 5
28 0.50 1.00 0.67 2
29 1.00 1.00 1.00 2
30 1.00 1.00 1.00 2
31 1.00 0.75 0.86 4
32 1.00 1.00 1.00 2
34 0.25 1.00 0.40 1
35 1.00 1.00 1.00 5
36 1.00 1.00 1.00 3
37 1.00 1.00 1.00 1
38 1.00 0.75 0.86 4
39 0.50 1.00 0.67 5
accuracy 0.81 120
macro avg 0.89 0.85 0.83 120
weighted avg 0.91 0.81 0.81 120
Confusion Matrix:
[[2 0 0 ... 0 0 0]
[0 2 0 ... 0 0 0]
[0 0 2 ... 0 0 1]
...
[0 0 0 ... 1 0 0]
[0 0 0 ... 0 3 0]
[0 0 0 ... 0 0 5]]
Cross-validation accuracy: 87.25%