DecisionTreeClassifier.predict源码

DecisionTreeClassifier.predict的源码如下：

def predict(self, X, check_input=True):
    """
    Predict class or regression value for X.

    For a classification model, the predicted class for each sample in X is
    returned. For a regression model, the predicted value based on X is
    returned.

    Parameters
    ----------
    X : array-like or sparse matrix of 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``.

    check_input : bool, (default=True)
        Allow to bypass several input checking.
        Don't use this parameter unless you know what you do.

    Returns
    -------
    y : array of shape = [n_samples] or [n_samples, n_outputs]
        The predicted classes, or the predict values.
    """
    check_is_fitted(self)

    X = self._validate_X_predict(X, check_input)
    proba = self.tree_.predict(X)
    n_samples = X.shape[0]

    if self.n_outputs_ == 1:
        return self.classes_.take(np.argmax(proba, axis=1),
                                  axis=0)
    else:
        class_type = self.classes_[0].__class__
        predictions = np.zeros((n_samples, self.n_outputs_),
                               dtype=class_type)
        for k in range(self.n_outputs_):
            predictions[:, k] = self.classes_[k].take(
                np.argmax(proba[:, k], axis=1),
                axis=0)
        return predictions

首先，它会检查分类器是否已经被拟合（通过调用`check_is_fitted`方法）。然后，它调用`_validate_X_predict`方法来验证输入数据，将其转换为`np.float32`类型，并将稀疏矩阵转换为稀疏`csr_matrix`矩阵。随后，它会调用决策树的`predict`方法，返回每个样本的预测概率。最后，它会根据分类器是否是回归模型，返回相应的预测值。如果分类器是回归模型，返回预测值；如果分类器是分类模型，返回预测的类别。