介绍:
工作原理:得到原始数据集,基于最好的属性值划分数据集,由于特征值可能多于两个,因此可能存在大于两个分支的数据集划分.第一次划分后,数据将被向下传递到树分支的下一个节点,在这个节点上,我们可以再次划分数据.因此可以采用递归的原则处理数据集.
递归结束的条件是:程序遍历完所有划分数据集的属性,或者每个分支下的所有实例都具有相同的分类.如果所有实例具有相同的分类,则得到一个叶子节点或者终止块.任何到达叶子节点的数据必然属于叶子节点的分类.
def majorityCnt(classList):
classCount={}
for vote in classList:
if vote not in classCount.keys(): classCount[vote] = 0
classCount[vote] += 1
sortedClassCount = sorted(classCount.iteritems(), key=operator.itemgetter(1), reverse=True)
return sortedClassCount[0][0]
复制代码
def createTree(dataSet,labels):
classList = [example[-1] for example in dataSet]
if classList.count(classList[0]) == len(classList):
return classList[0]#stop splitting when all of the classes are equal
if len(dataSet[0]) == 1: #stop splitting when there are no more features in dataSet
return majorityCnt(classList)
bestFeat = chooseBestFeatureToSplit(dataSet)
bestFeatLabel = labels[bestFeat]
myTree = {bestFeatLabel:{}}
del(labels[bestFeat])
featValues = [example[bestFeat] for example in dataSet]
uniqueVals = set(featValues)
for value in uniqueVals:
subLabels = labels[:] #copy all of labels, so trees don't mess up existing labels
myTree[bestFeatLabel][value] = createTree(splitDataSet(dataSet, bestFeat, value),subLabels)
return myTree
大家还在听
