chore: import upstream snapshot with attribution

examples/naive_bayes.py

@@ -0,0 +1,31 @@
+from sklearn.datasets import make_classification
+from sklearn.metrics import roc_auc_score
+from sklearn.model_selection import train_test_split
+
+from mla.naive_bayes import NaiveBayesClassifier
+
+
+def classification():
+    # Generate a random binary classification problem.
+    X, y = make_classification(
+        n_samples=1000,
+        n_features=10,
+        n_informative=10,
+        random_state=1111,
+        n_classes=2,
+        class_sep=2.5,
+        n_redundant=0,
+    )
+    X_train, X_test, y_train, y_test = train_test_split(
+        X, y, test_size=0.1, random_state=1111
+    )
+
+    model = NaiveBayesClassifier()
+    model.fit(X_train, y_train)
+    predictions = model.predict(X_test)[:, 1]
+
+    print("classification accuracy", roc_auc_score(y_test, predictions))
+
+
+if __name__ == "__main__":
+    classification()