示例Python代码如下: 代码语言:javascript 复制 from sklearn.feature_extractionimportDictVectorizerimportcsv from sklearnimporttree from sklearnimportpreprocessing from sklearn.externals.siximportStringIO # Readinthe csv file and put features into listofdict and listofclasslabelallElectronicsData=open(r'/home...
featValues = [example[bestFeat] for example in dataSet] uniqueVals = set(featValues) #递归输出决策树 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, valu...
一、基于原生Python实现决策树(Decision Tree) 决策树是一种基本的分类和回归方法,可以用于二元和多元分类以及连续和离散的数值预测。决策树的构建过程就是递归地选择最优的特征并根据该特征对数据进行分裂的过程,直到满足某种条件为止,然后构建出一颗决策树。在进行分类预测时,对输入数据从根节点开始沿着特定的路径向下...
for vote in classList: if vote not in classCount.keys(): classCount[vote] = 0 classCount[vote] += 1 return max(classCount) #基于递归构建决策树。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...
featList = [example[i] for example in dataSet]#create a list of all the examples of this feature uniqueVals = set(featList) #get a set of unique values newEntropy = 0.0 for value in uniqueVals: subDataSet = splitDataSet(dataSet, i, value) ...
fromsklearn.treeimportDecisionTreeClassifierfromsklearn.treeimportDecisionTreeClassifier# 训练决策树模型(控制决策树的深度, 这里控制最大深度是2)dtree=DecisionTreeClassifier(max_depth=2)dtree.fit(df,y)"""DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=2,max_features=None, max_le...
# 递归生成决策树节点def createTreeNode(dataSet, labels, featLabels): # 取出当前节点的样本的标签 -1 表示在最后一位 curLabelList = [example[-1] for example in dataSet] # --- 停止条件 --- # 1. 判断当前节点的样本的标签是不是已经全为1个值了,如果是则直接返回其唯一类别 if len(curLabel...
def createTree(dataSet, labels): classList = [example[-1] for example in dataSet] # 类别:男或女 if classList.count(classList[0]) == len(classList): return classList[0] if len(dataSet[0]) == 1: return majorityCnt(classList) ...
在Python环境中，利用scikit-learn库可以便捷地训练决策树模型。以下是一个基于Iris数据集训练决策树分类器的基本示例：from sklearn.datasets import load_iris from sklearn.tree import DecisionTreeClassifier # 加载数据 iris = load_iris()X = iris.data y = iris.target # 创建并训练模型 clf = Decision...
决策树是一种强大的预测方法。它们之所以受欢迎,是因为通常最终模型易于被所有利益相关者理解。最终决策树可以确切地解释为什么做出特定的预测,从而使其对于运营用途非常有吸引力。 CART 是 Classification and Regression Trees的简称,他的模型表现形式是二叉树,接下来我们来看看怎么用CART进行分类～ ...