from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import GaussianNB
from sklearn.metrics import accuracy_score
# Step 1: Load dataset (handwritten digits 0-9)
digits = load_digits()
X, y = digits.data, digits.target
# Step 2: Split into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
# Step 3: Apply Naive Bayes (Bayesian Inference)
model = GaussianNB()
model.fit(X_train, y_train)
# Step 4: Make predictions
y_pred = model.predict(X_test)
# Step 5: Measure accuracy
accuracy = accuracy_score(y_test, y_pred)
print(f"Accuracy: {accuracy:.2f}")
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