Logistic回归的实例
Logistic回归的使用和缺失值的处理
从疝气病预测病马的死亡率
数据集:
UCI上的数据,368个样本,28个特征
测试方法:
交叉测试
实现细节:
1.数据中因为存在缺失值所以要进行预处理,这点待会再单独谈
2.数据中本来有三个标签,这里为了简单直接将未能存活和安乐死合并了
3.代码中计算10次求均值
缺失值的处理:
一般来说有这么几种方法处理缺失值:
- 人工填写缺失值
- 使用全局变量填充缺失值
- 忽略有缺失值的样本
- 使用属性的中心度量(均值或中位数等)填充缺失值
- 使用与给定元祖同一类的所有样本的属性均值或中位数
- 使用最可能的值(需要机器学习算法推到)
对不同的数据我们要采用不同的方法,这里考虑到我们用Logistic回归那么我们可以采用0填充,因为用0在更新weight = weight + alpha * error * dataMatrix[randIndex]
的时候不会产生更新,并且sigmoid(0)=0.5,他对结果也不会产生影响。 - #coding=utf-8
from numpy import *def loadDataSet():
dataMat = []
labelMat = []
fr = open(‘testSet.txt’)
for line in fr.readlines():
lineArr = line.strip().split()
dataMat.append([1.0, float(lineArr[0]), float(lineArr[1])])
labelMat.append(int(lineArr[2]))
return dataMat, labelMat
def sigmoid(inX):
return 1.0/(1+exp(-inX))
def stocGradAscent1(dataMatrix, classLabels, numIter=150):
m,n = shape(dataMatrix)
#alpha = 0.001
weight = ones(n)
for j in range(numIter):
dataIndex = range(m)
for i in range(m):
alpha = 4/ (1.0+j+i) +0.01
randIndex = int(random.uniform(0,len(dataIndex)))
h = sigmoid(sum(dataMatrix[randIndex]*weight))
error = classLabels[randIndex] – h
weight = weight + alpha * error * dataMatrix[randIndex]
del(dataIndex[randIndex])
return weightdef classifyVector(inX, weights):
prob = sigmoid(sum(inX*weights))
if prob > 0.5: return 1.0
else: return 0.0def colicTest():
frTrain = open(‘horseColicTraining.txt’); frTest = open(‘horseColicTest.txt’)
trainingSet = []; trainingLabels = []
for line in frTrain.readlines():
currLine = line.strip().split(‘t’)
lineArr =[]
for i in range(21):
lineArr.append(float(currLine[i]))
trainingSet.append(lineArr)
trainingLabels.append(float(currLine[21]))
trainWeights = stocGradAscent1(array(trainingSet), trainingLabels, 1000)
errorCount = 0; numTestVec = 0.0
for line in frTest.readlines():
numTestVec += 1.0
currLine = line.strip().split(‘t’)
lineArr =[]
for i in range(21):
lineArr.append(float(currLine[i]))
if int(classifyVector(array(lineArr), trainWeights))!= int(currLine[21]):
errorCount += 1
errorRate = (float(errorCount)/numTestVec)
print “the error rate of this test is: %f” % errorRate
return errorRatedef multiTest():
numTests = 10; errorSum=0.0
for k in range(numTests):
errorSum += colicTest()
print “after %d iterations the average error rate is: %f” % (numTests, errorSum/float(numTests))
def main():
multiTest()
if __name__ == ‘__main__’:
main()
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