from sklearn.datasets import load_iris from sklearn import tree iris = load_iris() clf = tree.DecisionTreeClassifier() clf = clf.fit(iris.data, iris.target) 可视化需要安装python-graphviz,可以在命令行中输入 conda install python-graphviz 来安装,也可以在Anaconda navigator里面 “Search Packages”那...
import pandas as pd from sklearn.tree import DecisionTreeClassifier from sklearn.cross_validation import train_test_split from sklearn.metrics import classification_report from sklearn.pipeline import Pipeline from sklearn.grid_search import GridSearchCV import zipfile #压缩节省空间 z=zipfile.ZipFile...
字符,并把值转化为-1X.replace(to_replace='*\?', value=-1, regex=True, inplace=True) X_train,X_test,y_train,y_test=train_test_split(X,y)#用信息增益启发式算法建立决策树pipeline=Pipeline([('clf',DecisionTreeClassifier(criterion='entropy'))]) parameters={'clf__max_depth': (150, 155...
treeMean = (tree['left'] + tree['right'])/2 errorMerge = sum(power(testData[:,-1] - treeMean , 2 )) if errorMerge < errorNoMerge: return treeMean else: return tree return tree sklearn中使用决策树 中文文档如下: 在sklearn中,其选择的是优化后的CART算法 分类: from sklearn import t...
DecisionTreeClassifier 分类树 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...
1、使用sklearn构建决策树 在Jupyter Notebook导入相关库: fromsklearn.datasetsimportload_irisfromsklearnimporttreeimportsysimportosfromIPython.displayimportImageimportpydotplusimportpandasaspd IPython.display的Image和pydotplus是为了可视化生成的决策树。没安装pydotpplus可以使用pip3命令安装。
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 = DecisionTreeClassifier(max_depth=3, random_state=42)clf.fit(X, y)2. 模型可视化 决策树的可视化有助于理解模型的决策逻辑。可以使用graphviz库配合scikit-learn的...
sklearn 中DecisionTreeRegressor默认参数,1.导入相应包importpandasaspdfromsklearn.feature_selectionimportVarianceThresholdimportnumpyasnpfromsklearn.ensembleimportRandomForestClassifierasRFCfromsklearn.neighborsimportKNeighbor
sklearn.tree.DecisionTreeClassifier详细说明 sklearn.tree.DecisionTreeClassifier()函数⽤于构建决策树，默认使⽤CART算法，现对该函数参数进⾏说明，参考的是scikit-learn 0.20.3版本。sklearn.tree.DecisionTreeClassifier(criterion=’gini’, splitter=’best’, max_depth=None, min_samples_split=2, min_...
坚持,就是胜利!结果97.37%,居然和KNN算法一样。 截屏2020-05-27上午10.51.35.png 代码: importpandasaspdimportnumpyasnpfromsklearnimportmetricsfromsklearnimporttree# 决策树算法,函数名,tree.DecisionTreeClassifier()defmx_dtree(train_x,train_y):mx=tree.DecisionTreeClassifier()mx.fit(train_x,train_y)re...