INNER CODE UNIT · Python

predict

ankonzoid/LearningX · basic_ML/classification/decision_tree.py:90

    def predict(self, X):

        def traverse(node, x):
            if "split" in node.keys():
                if x[node["j_threshold"]] <= node["threshold"]:
                    return traverse(node["child_left"], x)
                else:
                    return traverse(node["child_right"], x)
            else:
                return node["value"]

        N, d = X.shape
        y_pred = []
        for i in range(N):
            y_pred_i = traverse(self.tree, X[i])
            y_pred.append(y_pred_i)
        y_pred = np.array(y_pred)
        return y_pred

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