这样的回归树通常称为最小二乘回归树(least squares regression tree)。 如果已将输入空间划分为M个区域 R_{1},R_{2},...,R_{M} ,并且在每个区域 R_{m} 上有一个固定的输出值 \hat{c}_{m} ,于是回归树模型可以表示为: f(x) = \sum_{m=1}^{M}{\hat{c}_{m}I(x\in R_{m})} ...
本文将介绍如何使用Python中的scikit-learn库来实现Boosted Regression Tree,并提供代码示例。 Boosted Regression Tree简介 Boosted Regression Tree是一种集成学习算法,它通过逐步迭代地训练一系列弱回归树来提高预测性能。在每一轮迭代中,算法根据之前的模型表现来调整样本权重,使得模型能够更好地拟合数据。最终,各个弱回...
tree['left']= getMean(tree['left'])return (tree['left']+tree['right'])/2.0# 检查是否适合合并分枝defprune(tree,testData):"""Desc:从上而下找到叶节点,用测试数据集来判断将这些叶节点合并是否能降低测试误差Args:tree -- 待剪枝的树testData -- 剪枝所需要的测试数据 testDataReturns:tree -- ...
tree['left']=getMean(tree['left'])ifisTree(tree['right']): tree['right']=getMean(tree['right'])return(tree['left']+tree['right'])/2.0## 剪枝处理defprune(tree, testData):#如果test的目标值为0,那么根据数结构,获取左右数据ifshape(testData)[0]==0:returngetMean(tree)#如果左右子树存...
```python model = DecisionTreeRegressor(random_state=42) model.fit(X_train, y_train) ``` 四、输出决策树回归规则 使用export_text函数可以输出决策树回归规则。具体步骤如下: 1. 创建一个空字符串变量,用于存储规则输出。 2. 使用DecisionTreeRegressor类的predict函数对测试集进行预测,并获取预测结果。 3...
逻辑回归模型(Logistic Regression)及Python实现 http://www.cnblogs.com/sumai 1.模型 在分类问题中,比如判断邮件是否为垃圾邮件,判断肿瘤是否为阳性,目标变量是离散的,只有两种取值,通常会编码为0和1。假设我们有一个特征X,画出散点图,结果如下所示。这时候如果我们用线性回归去拟合一条直线:hθ(X) = θ0+...
1、读入数据import randomimport numpy as npimport matplotlib.pyplot as pltimport torchimport torch.nn as nnimport torch.nn.functional as Fx_train_list = []y_train_list = []for i in range(1, 50): x = i*random.choice([0.7,0.8,0.9]) y = i*random.choice([0.4,0 ...
https:///thomas-haslwanter/statsintro_python/tree/master/ISP/Code_Quantlets/07_CheckNormality_CalcSamplesize/checkNormality In tests for normality, different challenges can arise: sometimes only few samples may be available, while other times one may have many data, but some extremely ...
reshape(y, (m_samples, 1)) # 梯度训练n_iterations轮 for i in range(self.n_iterations): h_x = X.dot(self.w) y_pred = sigmoid(h_x) w_grad = X.T.dot(y_pred - y) self.w = self.w - self.learning_rate * w_grad 逻辑回归的主要核心函数吗,X加多一列全1的值,这样wx相当于wx...
Optional complexity parameter for pruning. If prune_cp > 0, prune.rxDTree is called on the completed tree with the specified prune_cp and the pruned tree is returned. This contrasts with the cp parameter that determines which splits are considered in growing the tree. The option prune_cp=”...