宁波网站建设果核,室内设计多少钱,网站上的销售怎么做的,wordpress企业站实例1 主要思想
1.1 数据 1.2 训练和使用模型
训练#xff1a;建立模型#xff08;树#xff09; 测试#xff1a;使用模型#xff08;树#xff09; Weka演示ID3#xff08;终端用户模式#xff09;
双击weka.jar选择Explorer载入weather.arff选择trees–ID3构建树…1 主要思想
1.1 数据 1.2 训练和使用模型
训练建立模型树 测试使用模型树 Weka演示ID3终端用户模式
双击weka.jar选择Explorer载入weather.arff选择trees–ID3构建树观察结果
建立决策树流程
Step 1. 选择一个属性Step 2. 将数据集分成若干子集Step 3.1 对于决策属性值唯一的子集, 构建叶结点Step 3.2 对于决策属性值不唯一的子集, 递归调用本函数
演示: 利用txt文件, 按照决策树的属性划分数据集
2 信息熵
问题: 使用哪个属性进行数据的划分? 随机变量YYY的信息熵为 (YYY为决策变量) H(Y)E[I(yi)]∑i1np(yi)log1p(yi)−∑i1np(yi)logp(yi),H(Y) E[I(y_i)] \sum_{i1}^n p(y_i)\log \frac{1}{p(y_i)} - \sum_{i1}^n p(y_i)\log p(y_i), H(Y)E[I(yi)]i1∑np(yi)logp(yi)1−i1∑np(yi)logp(yi), 其中 0log000 \log 0 00log00. 随机变量YYY关于XXX的条件信息熵为(XXX为条件变量) H(Y∣X)∑i1mp(xi)H(Y∣Xxi)−∑i,jp(xi,yj)logp(yj∣xi).\begin{array}{ll} H(Y | X) \sum_{i1}^m p(x_i) H(Y | X x_i)\\ - \sum_{i, j} p(x_i, y_j) \log p(y_j | x_i). \end{array} H(Y∣X)∑i1mp(xi)H(Y∣Xxi)−∑i,jp(xi,yj)logp(yj∣xi). XXX为YYY带来的信息增益: H(Y)−H(Y∣X)H(Y) - H(Y | X)H(Y)−H(Y∣X).
3 程序分析
版本1. 使用sklearn (调包侠) 这里使用了数据集是数值型。
import numpy as np
import scipy as sp
import time, sklearn, math
from sklearn.model_selection import train_test_split
import sklearn.datasets, sklearn.neighbors, sklearn.tree, sklearn.metricsdef sklearnDecisionTreeTest():#Step 1. Load the datasettempDataset sklearn.datasets.load_breast_cancer()x tempDataset.datay tempDataset.target# Split for training and testingx_train, x_test, y_train, y_test train_test_split(x, y, test_size 0.2)#Step 2. Build classifiertempClassifier sklearn.tree.DecisionTreeClassifier(criterionentropy)tempClassifier.fit(x_train, y_train)#Step 3. Test#precision, recall, thresholds sklearn.metrics.precision_recall_curve(y_test, tempClassifier.predict(x_test))tempAccuracy sklearn.metrics.accuracy_score(y_test, tempClassifier.predict(x_test))tempRecall sklearn.metrics.recall_score(y_test, tempClassifier.predict(x_test))#Step 4. Outputprint(precision {}, recall {}.format(tempAccuracy, tempRecall))sklearnDecisionTreeTest()版本2. 自己重写重要函数
信息熵
#计算给定数据集的香农熵
def calcShannonEnt(paraDataSet):numInstances len(paraDataSet)labelCounts {} #定义空字典for featVec in paraDataSet:currentLabel featVec[-1]if currentLabel not in labelCounts.keys():labelCounts[currentLabel] 0labelCounts[currentLabel] 1shannonEnt 0.0for key in labelCounts:prob float(labelCounts[key])/numInstancesshannonEnt - prob * math.log(prob, 2) #以2为底return shannonEnt划分数据集
#dataSet 是数据集axis是第几个特征value是该特征的取值。
def splitDataSet(dataSet, axis, value):resultDataSet []for featVec in dataSet:if featVec[axis] value:#当前属性不需要reducedFeatVec featVec[:axis]reducedFeatVec.extend(featVec[axis1:])resultDataSet.append(reducedFeatVec)return resultDataSet选择最好的特征划分
#该函数是将数据集中第axis个特征的值为value的数据提取出来。
#选择最好的特征划分
def chooseBestFeatureToSplit(dataSet):#决策属性不算numFeatures len(dataSet[0]) - 1baseEntropy calcShannonEnt(dataSet)bestInfoGain 0.0bestFeature -1for i in range(numFeatures):#把第i列属性的值取出来生成一维数组featList [example[i] for example in dataSet]#剔除重复值uniqueVals set(featList)newEntropy 0.0for value in uniqueVals:subDataSet splitDataSet(dataSet, i, value)prob len(subDataSet) / float(len(dataSet))newEntropy prob*calcShannonEnt(subDataSet)infoGain baseEntropy - newEntropyif(infoGain bestInfoGain):bestInfoGain infoGainbestFeature ireturn bestFeature构建叶节点
#如果剩下的数据中无特征则直接按最大百分比形成叶节点
def majorityCnt(classList):classCount {}for vote in classList:if vote not in classCount.keys():classCount[vote] 0classCount 1;sortedClassCount sorted(classCount.iteritems(), key operator.itemgette(1), reverse True)return sortedClassCount[0][0]创建决策树
#创建决策树
def createTree(dataSet, paraFeatureName):featureName paraFeatureName.copy()classList [example[-1] for example in dataSet]#Already pureif classList.count(classList[0]) len(classList):return classList[0]#No more attributeif len(dataSet[0]) 1:#if len(dataSet) 1:return majorityCnt(classList)bestFeat chooseBestFeatureToSplit(dataSet)#print(dataSet)#print(bestFeat:, bestFeat)bestFeatureName featureName[bestFeat]myTree {bestFeatureName:{}}del(featureName[bestFeat])featvalue [example[bestFeat] for example in dataSet]uniqueVals set(featvalue)for value in uniqueVals:subfeatureName featureName[:]myTree[bestFeatureName][value] createTree(splitDataSet(dataSet, bestFeat, value), subfeatureName)return myTree分类和返回预测结果
#Classify and return the precision
def id3Classify(paraTree, paraTestingSet, featureNames, classValues):tempCorrect 0.0tempTotal len(paraTestingSet)tempPrediction classValues[0]for featureVector in paraTestingSet:print(Instance: , featureVector)tempTree paraTreewhile True:for feature in featureNames:try:tempTree[feature]splitFeature featurebreakexcept:i 1 #Do nothingattributeValue featureVector[featureNames.index(splitFeature)]print(splitFeature, , attributeValue)tempPrediction tempTree[splitFeature][attributeValue]if tempPrediction in classValues:breakelse:tempTree tempPredictionprint(Prediction , tempPrediction)if featureVector[-1] tempPrediction:tempCorrect 1return tempCorrect/tempTotal构建测试代码
def mfID3Test():#Step 1. Load the datasetweatherData [[Sunny,Hot,High,FALSE,N],[Sunny,Hot,High,TRUE,N],[Overcast,Hot,High,FALSE,P],[Rain,Mild,High,FALSE,P],[Rain,Cool,Normal,FALSE,P],[Rain,Cool,Normal,TRUE,N],[Overcast,Cool,Normal,TRUE,P],[Sunny,Mild,High,FALSE,N],[Sunny,Cool,Normal,FALSE,P],[Rain,Mild,Normal,FALSE,P],[Sunny,Mild,Normal,TRUE,P],[Overcast,Mild,High,TRUE,P],[Overcast,Hot,Normal,FALSE,P],[Rain,Mild,High,TRUE,N]]featureName [Outlook, Temperature, Humidity, Windy]classValues [P, N]tempTree createTree(weatherData, featureName)print(tempTree)#print(createTree(mydata, featureName))#featureName [Outlook, Temperature, Humidity, Windy]print(Before classification, feature names , featureName)tempAccuracy id3Classify(tempTree, weatherData, featureName, classValues)print(The accuracy of ID3 classifier is {}.format(tempAccuracy))def main():sklearnDecisionTreeTest()mfID3Test()main()4 讨论
符合人类思维的模型 信息增益只是一种启发式信息 与各个属性值“平行”的划分。
其它决策树
C4.5处理数值型数据CART使用gini指数
