这里的label更多是对于分类特征的名字,为了更好看和后面的理解。 def createTree(dataSet, labels): classList = [example[-1] for example in dataSet] if classList.count(classList[0]) ==len(classList):#类别相同则停止划分 return classList[0] if len(dataSet[0]) == 1:#所有特征已经用完 return ...
tree[bestlabel][value]=createTree(singlesplit(data,bestfuture,value),sublabel)returntreedefclassifier(inputree,featurelabel,clsdata): firststr=inputree.keys()[0] secondict=inputree[firststr] classlabel=''featindex=featurelabel.index(firststr)forkeyinsecondict.keys():ifclsdata[featindex]==key:...
#coding:utf-8importpandas as pdfromsklearn.ensembleimportRandomForestClassifierfromsklearn.cross_validationimporttrain_test_splitfromsklearn.metricsimportclassification_reportfromsklearn.pipelineimportPipelinefromsklearn.grid_searchimportGridSearchCV df=pd.read_csv('.\\tree_data\\ad.data',header=None,low_...
dataSet, labels = createDataSet() createTree(dataSet, labels) [out] {'no surfacing': {0: 'no', 1: {'flippers': {0: 'no', 1: 'yes'}}} 使用文本注解绘制树节点 代码语言:python 代码运行次数:0 运行 AI代码解释 # -.- coding:utf-8 -.-import matplotlib.pyplot as pltimport matplotlib...
write_png('tree.png') # cmd: dot -Tpdf tree.dot -o output.pdf,dot -Tpng tree.dot -o output.png 决策树可视化 附. 本文完整代码 代码语言:javascript 代码运行次数:0 运行 AI代码解释 # -*- coding:utf-8 -*- # @Python Version: 3.7 # @Time: 2020/3/13 19:36 # @Author: Michael...
tree.py # coding=utf-8 from math import log import operator import treePlotter def calcShannonEnt(dataSet): """ 计算香农信息增益 :param dataSet:输入的数据集 :return: 熵 """ numEntries = len(dataSet) # 数据集实例总数 labelCounts = {} # 数据字典,键值是最后一列的数值,记录当前类别出现的次...
决策树 decision tree 算法原理: 构造一个决策树,即通过特征构造一个树,如下图。沿着树结构访问树节点直到得到数据的分类结果。构造树时先找出一个决定性的特征,这个特征把数据分为几个数据子集,即分支节点,如果分支节点的数据全部属于同一类,那么这个分类已结束,否则再进步分类。找出这个最佳(决定性的)特征很重要,...
In this tutorial, learn Decision Tree Classification, attribute selection measures, and how to build and optimize Decision Tree Classifier using Python Scikit-learn package. Updated Jun 27, 2024 · 12 min read Contents The Decision Tree Algorithm How Does the Decision Tree Algorithm Work? Attribute...
训练数据:使用createTree()函数 测试算法:编写测试函数验证决策树可以正确分类给定的数据实例 使用算法:储存树的数据结构,以便下次使用的时候不用再重新构造树结构 代码 在编码的过程中,风采依旧,恩,入坑了 当然已经填上啦 #!/usr/bin/env python3# -*- coding: utf-8 -*-' a DT module '__author__ = ...
This article demonstrates a step-by-step approach to the applications of python to evaluate the performance of decision tree-based gradient boosting machine (gbm), lightgbm, extreme gradient boosting (xgboost), arid adaptive boosting (adaboost) algorithms for predicting the in-bus carbon dioxide ...