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...
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...
dummyY)print("clf: "+str(clf))# Visualize modelwithopen("allElectronicInformationGainOri.dot",'w')asf:f=tree.export_graphviz(clf,feature_names=vec.get_feature_names(),out_file=f)oneRowX=dummyX[0,:]print("oneRowX: "+str(oneRowX))newRowX=oneRowX...
Decision Trees, in general, suffer from overfitting. Quite often, left to it's own devices, a Decision Tree model will overfit, and therefore, we need to think about how best to avoid overfitting; this is done to avoid complexity. A simple model will more often work better in practice th...
DecisionTree本身包含随机化过程。 划分训练集和测试集的方式没有设置随机种子。 算法的随机性可以通过以下形式表达: [ RandomValue = \frac{\text{Current Time}}{\text{Number of Seeds}} ] 配置对比差异如下: -model = DecisionTreeClassifier()+model = DecisionTreeClassifier(random_state=42) ...
fromsklearn.model_selectionimportGridSearchCV# 设置参数范围param_grid={'max_depth':[None,3,5,10],'min_samples_split':[2,5,10],'min_samples_leaf':[1,2,5]}# 创建GridSearchCV对象grid_search=GridSearchCV(DecisionTreeClassifier(),param_grid,cv=5)grid_search.fit(X_train,y_train)# 输出...
决策树代码(Spark Python) 代码里数据:https://pan.baidu.com/s/1jHWKG4I密码:acq1 #-*-coding=utf-8 -*-frompysparkimportSparkConf, SparkContext sc= SparkContext('local')frompyspark.mllib.treeimportDecisionTree, DecisionTreeModelfrompyspark.mllib.utilimportMLUtils#Load and parse the data file ...
model.fit(x_train,y_train) model.score(x_test,y_test),model.score(x_train,y_train) >>`(0.77094972067039103, 0.9845505617977528)` 发现数据过拟合,此处分别对最大深度及信息增益阀值进行分类讨论 4.1 设置最大深度: def m_scores(depth): model=DecisionTreeClassifier(max_depth=depth) ...
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A python library for decision tree visualization and model interpretation. Decision trees are the fundamental building block ofgradient boosting machinesandRandom Forests(tm), probably the two most popular machine learning models for structured data. Visualizing decision trees is a tremendous aid when lea...