Create xgboost_classifier_custom.py

machine_learning/xgboost_classifier_custom.py

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+import numpy as np
+from sklearn.tree import DecisionTreeClassifier
+
+class CustomXGBoostClassifier:
+    def __init__(self, n_estimators=100, learning_rate=0.1, max_depth=3):
+        self.n_estimators = n_estimators
+        self.learning_rate = learning_rate
+        self.max_depth = max_depth
+        self.trees = []
+
+    def fit(self, x, y):
+        n_samples, n_features = x.shape
+        y = np.where(y == 0, -1, 1)  # Convert 0/1 labels to -1/1
+
+        predictions = np.zeros(n_samples)
+
+        for _ in range(self.n_estimators):
+            residual = y - predictions
+            tree = DecisionTreeClassifier(max_depth=self.max_depth)
+            tree.fit(x, residual)
+            tree_predictions = tree.predict(x)
+            predictions += self.learning_rate * tree_predictions
+            self.trees.append(tree)
+
+    def predict(self, x):
+        result = np.zeros(x.shape[0])
+
+        for tree in self.trees:
+            result += self.learning_rate * tree.predict(x)
+
+        return np.where(result >= 0, 1, 0)
+
+# Example Usage:
+# clf = CustomXGBoostClassifier()
+# clf.fit(X_train, y_train)
+# predictions = clf.predict(X_test)