Python sklearn.employee.GradientBoostingClassifier.predict（）是如何工作的？

Question

Pythonsklearn.ensemble.GradientBoostingClassifier.predict()是如何工作的

将实例分配到目标级别的predict()阈值是多少？假设，目标是1、2、3或4，并且目标1/2/3/4的实例的预测概率是0.2/0.5/0.1/0.2。predict()是否将此实例分配给目标级别2

下面是源代码。如何逐行或逐块解释它？我还附上了predict()及其源代码的链接

def predict(self, X):
    """Predict class for X.
    Parameters
    ----------
    X : {array-like, sparse matrix}, shape (n_samples, n_features)
        The input samples. Internally, it will be converted to
        ``dtype=np.float32`` and if a sparse matrix is provided
        to a sparse ``csr_matrix``.
    Returns
    -------
    y : array, shape (n_samples,)
        The predicted values.
    """
    raw_predictions = self.decision_function(X)
    encoded_labels = \
        self.loss_._raw_prediction_to_decision(raw_predictions)
    return self.classes_.take(encoded_labels, axis=0)

• https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#sklearn.ensemble.GradientBoostingClassifier.predict

• https://github.com/scikit-learn/scikit-learn/blob/1495f6924/sklearn/ensemble/gradient_boosting.py#L2118