Decision_tree-python 决策树分类(ID3,C4.5,CART) 三种算法的区别如下: (1) ID3算法以信息增益为准则来进行选择划分属性,选择信息增益最大的; (2) C4.5算法先从候选划分属性中找出信息增益高于平均水平的属性,再从中选择增益率最高的; (3) CART算法使用“基尼指数”来选择划分属性,选择基尼值最小的属性作为划分...
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2.4 buildDecisionTree() defbuildDecisionTree(rows,evaluationFunction=gini):#递归建立决策树,当gain = 0 时停止递归#bulid decision tree by recursive function#stop recursive function when gain = 0#return treecurrentGain=evaluationFunction(rows)column_length=len(rows[0])rows_length=len(rows)best_gain=...
clf=DecisionTreeClassifier()clf.create_tree(X,Y,lense_labels) 查看生成的决策树: In[2]:clf.treeOut[2]:{'tearRate':{'normal':{'astigmatic':{'no':{'age':{'pre':'soft','presbyopic':{'prescript':{'hyper':'soft','myope':'no lenses'}},'young':'soft'}},'yes':{'prescript':{'h...
最近打算系统学习下机器学习的基础算法,避免眼高手低,决定把常用的机器学习基础算法都实现一遍以便加深印象。本文为这系列博客的第一篇,关于决策树(Decision Tree)的算法实现,文中我将对决策树种涉及到的 算法进行总结并附上自己相关的实现代码。所有算法代码以及用于相应模型的训练的数据都会放到GitHub上(https://gi...
本文为这系列博客的第一篇,关于决策树(Decision Tree)的算法实现,文中我将对决策树种涉及到的算法进行总结并附上自己相关的实现代码。所有算法代码以及用于相应模型的训练的数据都会放到GitHub上(https://github.com/PytLab/MLBox). 本文中我将一步步通过MLiA的隐形眼镜处方数集构建决策树并使用Graphviz将决策树...
完整代码地址:https://github.com/apachecn/MachineLearning/blob/master/src/python/3.DecisionTree/DecisionTree.py 项目案例2: 使用决策树预测隐形眼镜类型 项目概述 隐形眼镜类型包括硬材质、软材质以及不适合佩戴隐形眼镜。我们需要使用决策树预测患者需要佩戴的隐形眼镜类型。
Python实现决策树(Decision Tree)分类 https://machinelearningmastery.com/implement-decision-tree-algorithm-scratch-python/中给出了CART(Classification and Regression Trees,分类回归树算法,简称CART)算法的Python实现,采用的数据集为Banknote Dataset,这里在原作者的基础上,进行了略微改动,使其可以直接执行,code如下:...
As always, the code used in this tutorial is available on myGitHub. With that, let’s get started! How to Fit a Decision Tree Model using Scikit-Learn In order to visualize decision trees, we need first need to fit a decision tree model using scikit-learn. If this section is not clea...
3.1 Python代码 代码语言:javascript 代码运行次数:0 运行 AI代码解释 class Node(): def __init__(self, root=True, label=None, feature_name=None, feature=None): self.root = root self.label = label self.feature_name = feature_name self.feature = feature self.tree = {} self.result = { ...