max_features限制分枝时考虑的特征个数,超过限制个数的特征都会被舍弃。和max_depth异曲同工, max_features是用来限制高维度数据的过拟合的剪枝参数,但其方法比较暴力,是直接限制可以使用的特征数量而强行使决策树停下的参数,在不知道决策树中的各个特征的重要性的情况下,强行设定这个参数可能会导致模型 学习不足 0...
树型属性文档(DecisionTreeRegressor类)是指用于决策树回归模型的属性文档。决策树回归是一种基于树形结构的机器学习算法,用于解决回归问题。该算法通过构建一棵决策树来预测连续型目标变量的值。 决策树回归模型的主要属性包括: criterion(划分标准):用于衡量节点划分质量的指标。常见的划分标准有均方误差(MSE)和平...
3. max_depth:用于限制树深度的参数。如果不设置,则表示无限制。4. min_samples_split:用于控制节点分裂所需最小样本数目。如果某个节点中样本数量小于该值,则不再进行分裂。5. min_samples_leaf:用于控制叶子节点所需最小样本数目。如果某个叶子节点中样本数量小于该值,则会与兄弟节点合并。6. max_features:...
classsklearn.tree.DecisionTreeClassifier(*, criterion='gini', splitter='best', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=None, random_state=None, max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, class_weight...
classsklearn.tree.DecisionTreeClassifier(criterion=’gini’,splitter=’best’,max_depth=None, min_samples_split=2,min_samples_leaf=1,min_weight_fraction_leaf=0.0,max_features=None, random_state=None,max_leaf_nodes=None,min_impurity_decrease=0.0,min_impurity_split=None, ...
sklearn.tree.DecisionTreeClassifier(criterion=’gini’,splitter=’best’,max_depth=None,min_samples_split=2,min_samples_leaf=1,min_weight_fraction_leaf=0.0,max_features=None,random_state=None,max_leaf_nodes=None,min_impurity_decrease=0.0,min_impurity_split=None,class_weight=None,presort=False) ...
from sklearn import tree from IPython.display import Image import pydotplus 数据读到DataFrame里面,sc_459和sc_461是本次用到的两个评分。 clf = tree.DecisionTreeClassifier(max_features=2, class_weight="balanced", max_depth=2, min_samples_leaf=100) ...
一、Go语言简介 如果你是Go语言新手，或如果你对"并发(Concurrency)不是并行(parallelism)"这句话毫无赶...
1、首先来研究下DecisionTreeClassifier模块中的参数列表 DecisionTreeClassifier(criterion='gini',splitter='best',max_depth=None,min_samples_split=2,min_samples_leaf=1,min_weight_fraction_leaf=0.0,max_features=None,random_state=None,max_leaf_nodes=None,min_impurity_split=1e-07,class_weight=None,pre...
1、回归树DecisionTreeRegressor 的参数、属性、接口介绍 sklearn.tree.DecisionTreeRegressor ( criterion=’mse’ , splitter=’best’ , max_depth=None , min_samples_split=2 , min_samples_leaf=1 , min_weight_fraction_leaf=0.0 , max_features=None ...