[置顶] 决策树之数据划分

这篇文章利用了信息熵计算的东西,先写一个数据划分的东西，先写一个简单的逻辑划分：

def splitDataSet(dataSet, axis, value):
retDataSet = []
for featVec in dataSet:
if featVec[axis] == value:
reducedFeatVec = featVec[:axis]
reducedFeatVec.extend(featVec[axis+1:])
retDataSet.append(reducedFeatVec)

这是最简单的一个逻辑划分，首先建立一个新的数据集，将划分后的数据添入该数据集，

下面介绍选择最最好的数据集划分方式：

1.建立该列的变化标签

2.计算每种划分的信息熵

对上面步骤进行循环。

下面贴出该步骤的代码：

def chooseBestFeatureToSplit(dataSet):
numberFeatures = len(dataSet[0])-1
baseEntropy = calcShannonEnt(dataSet)
bestInfoGain = 0.0;
bestFeature = -1;
for i in range(numberFeatures):
featList = [example[i] for example in dataSet]
print(featList)
uniqueVals = set(featList)
print(uniqueVals)
newEntropy =0.0
for value in uniqueVals:
subDataSet = splitDataSet(dataSet, i, value)
prob = len(subDataSet)/float(len(dataSet))
newEntropy += prob * calcShannonEnt(subDataSet)
infoGain = baseEntropy - newEntropy
if(infoGain > bestInfoGain):
bestInfoGain = infoGain
bestFeature = i
return bestFeature

下面给个运行结果截图：