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...
classCount[vote]=0classCount[vote]+=1sortedClassCount =sorted(classCount.items(),key=operator.itemgetter(1),reverse=True)returnsortedClassCount[0][0]defcreateTree(dataSet,labels): classList=[example[-1]forexampleindataSet]# 类别:男或女ifclassList.count(classList[0])==len(classList):returnclassL...
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...
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...
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] ...
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) ...
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～ ...
二、Python实践 假如有以下简单样本,根据天气、是否周末、是否促销三个因素,用决策树预测销量高低。 假如样本比较理想,没有脏数据及缺失值等,省去数据清洗的步骤。 以上代码模型已建立好,并用测试集检测模型预测能力,结果如下(模型计算有小数四舍五入):
To easily run all the example code in this tutorial yourself, you can create a DataLab workbook for free that has Python pre-installed and contains all code samples. For a video explainer on Decision Tree Classification, you watch this DataCamp course video. Become a ML Scientist Master Python...
To easily run all the example code in this tutorial yourself, you can create a DataLab workbook for free that has Python pre-installed and contains all code samples. For a video explainer on Decision Tree Classification, you watch this DataCamp course video. Become a ML Scientist Master Python...