决策树结合网格搜索交叉验证

如下是常见的模型评估的指标定义及决策树结合网格搜索交叉验证的例子。详见下文：

混淆矩阵：

准确率:

精准率（预测为正样本真实也是正例的比值，又称为查准率）:

召回率(真实为正例的样本中预测为正例的比值，又称为查全率)：

F1 Socre （反映模型的稳健型):

###as_matrix
import pandas as pd
from sklearn import tree
from sklearn.tree import export_graphviz
import graphvizdef decisontreeSimple():filename= '../input/sales_data.xls'data = pd.read_excel(filename,index_col=u'序号')##print(data)data[data == u'好'] = 1data[data==u'是'] = 1data[data==u'高'] = 1data[data!=1] = -1x= data.iloc[:,:3].values.astype(int)y= data.iloc[:,3].values.astype(int)import sklearn.model_selection as cross_validationtrain_data, test_data, train_target, test_target = cross_validation.train_test_split(x, y, test_size=0.3,train_size=0.7,random_state=67897)  # 划分训练集和测试集from sklearn.tree import DecisionTreeClassifier as DTCdtc = DTC(criterion='entropy')dtc.fit(train_data, train_target)'''train_est = dtc.predict(train_data)  # 用模型预测训练集的结果train_est_p = dtc.predict_proba(train_data)[:, 1]  # 用模型预测训练集的概率test_est = dtc.predict(test_data)  # 用模型预测测试集的结果test_est_p = dtc.predict_proba(test_data)[:, 1]  # 用模型预测测试集的概率res_pd = pd.DataFrame({'test_target': test_target, 'test_est': test_est, 'test_est_p': test_est_p}).T  # 查看测试集预测结果与真实结果对比pd.set_option('precision', 2)pd.set_option('max_colwidth', 20)pd.set_option('display.max_columns', 20)print(res_pd)import sklearn.metrics as metricsprint(metrics.confusion_matrix(test_target, test_est, labels=[0, 1]))  # 混淆矩阵print(metrics.classification_report(test_target, test_est))  # 计算评估指标print(pd.DataFrame(list(zip(data.columns, dtc.feature_importances_))))  # 变量重要性指标
'''import sklearn.metrics as metricsfrom sklearn.model_selection import GridSearchCVimport matplotlib.pyplot as pltimport sklearn.tree as treeparam_grid = {'criterion': ['entropy','gini'],'max_depth': [3, 4, 5, 6, 7, 8],'min_samples_split': [4,5,6,7,8,9, 12, 16, 20, 24]}clf = tree.DecisionTreeClassifier(criterion='entropy')clfcv = GridSearchCV(estimator=clf, param_grid=param_grid,scoring='roc_auc', cv=4)clfcv.fit(train_data, train_target)# %%# 查看模型预测结果train_est = clfcv.predict(train_data)  # 用模型预测训练集的结果train_est_p = clfcv.predict_proba(train_data)[:, 1]  # 用模型预测训练集的概率test_est = clfcv.predict(test_data)  # 用模型预测测试集的结果test_est_p = clfcv.predict_proba(test_data)[:, 1]  # 用模型预测测试集的概率# %%fpr_test, tpr_test, th_test = metrics.roc_curve(test_target, test_est_p)fpr_train, tpr_train, th_train = metrics.roc_curve(train_target, train_est_p)plt.figure(figsize=[6, 6])plt.plot(fpr_test, tpr_test, color='blue')plt.plot(fpr_train, tpr_train, color='red')plt.title('ROC curve')plt.show()print(clfcv.best_params_)clf = tree.DecisionTreeClassifier(criterion='entropy', max_depth=3, min_samples_split=6)  # 当前支持计算信息增益和GINIclf.fit(train_data, train_target)  # 使用训练数据建模'''from sklearn.tree import export_graphvizfrom sklearn.externals.six import StringIOx = pd.DataFrame(x)print(x)with open("../output/tree.dot",'w') as f:f= export_graphviz(dtc,feature_names=x.columns,out_file=f)'''x = pd.DataFrame(x,columns=[u"weather",u"weekend",u"promotion"])#x = pd.DataFrame(x)dot_data = export_graphviz(dtc,feature_names=x.columns,out_file=None)graph = graphviz.Source(dot_data)''' dot文件里追加中文字体支持,需要手动编辑该文件graph [bb="0,0,712,365"];下面追加edge[fontname = "SimHei"];node[fontname = "SimHei"];'''##graph.render(filename="../output/tree",format="dot",cleanup="False")##直接转PDF会有乱码##graph.render(filename="../output/tree",format="pdf",cleanup="False")'''或者直接执行，但是需要先dot配置环境变量'''import osos.system('dot -Tpdf "../output/tree33.dot" -o "../output/tree33.pdf"')'''或者直接执行'''#with open("../output/tree.dot",'w') as f:#    f = export_graphviz(dtc,feature_names=x.columns,out_file=f)with open("../output/tree33.dot",'w') as f:f = export_graphviz(clf,feature_names=x.columns,out_file=f)##代码参考至<<Python机器学习及实践_从零开始通往KAGGLE竞赛之路>>
def decisontreeTitanic():##titanic=pd.read_csv('http://biostat.mc.vanderbilt.edu/wiki/pub/Main/DataSets/titanic.txt')titanic = pd.read_csv('../input/titanic.txt')print("行数:"+str(titanic.shape[0])+"\t"+"列数:"+str(titanic.shape[1])+"\t"+"行数："+str(len(titanic))+"\t"+"总数:"+str(titanic.size))'''row.names pclass  survived    name    age embarked    home.dest   room    ticket  boat    sex序号           乘客等级    获救情况        姓名      年龄  登船港口        目的地     房间号 船票信息    票价      性别'''#print(titanic[:7])#print(titanic.info())X = titanic[['pclass','age','sex']]y = titanic['survived']print(X[X['age'].notnull()]['age'].sum()/X[X['age'].notnull()].shape[0]) #19745.9166/636#X=X['age'].fillna(X['age'].mean(),inplace=True)#cc1 = X['age'].fillna(age_mean, inplace=True)#print(cc1)#X.info()X= X.copy() #要先拷贝，然后再进行fillna操作。X['age'].fillna(X['age'].mean(), inplace=True)print(X[:20])from sklearn.model_selection import train_test_splitX_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.01,random_state=33)print(X_test)from sklearn.feature_extraction import DictVectorizervec = DictVectorizer(sparse=False)##转换特征后，类别型特征都被剥离出单独的特征，数值型的保持不变X_train = vec.fit_transform(X_train.to_dict(orient='record'))print(vec.feature_names_)X_test = vec.fit_transform(X_test.to_dict(orient='record'))from sklearn.tree import DecisionTreeClassifierdtc = DecisionTreeClassifier()dtc.fit(X_train,y_train)y_predict = dtc.predict(X_test)#y_pd = pd.concat(pd.DataFrame([X_test]),pd.DataFrame([y_predict]),axis=1)print(y_predict)print(type(y_predict))#import numpy as np#print(np.concatenate((X_test,y_predict),axis=1))X_test_df = pd.DataFrame(X_test)y_predict_df = pd.DataFrame(y_predict)print("################")#print(pd.concat([X_test_df,y_predict_df],axis=0))'''print(X_test.ndim )print(y_predict.ndim)'''''' 如果对所有字段都应用这个规则，可以沿用如下写法for column in list(X.columns[X.isnull().sum() > 0]):mean_val = X[column].mean()X[column].fillna(mean_val, inplace=True)'''###模型评估from sklearn.metrics import classification_reportprint(dtc.score(X_test,y_test))print(classification_report(y_predict,y_test,target_names=['died','survived']))def irisdt():from sklearn import treefrom sklearn import model_selectionfrom sklearn.datasets import load_iris#from sklearn.grid_search import GridSearchCVfrom sklearn.model_selection import GridSearchCVfrom sklearn.metrics import classification_reportimport matplotlib.pyplot as pltiris = load_iris()x = iris.datay = iris.targetX_train, X_test, y_train, y_test = model_selection \.train_test_split(x, y, test_size=0.2,random_state=123456)parameters = {'criterion': ['gini', 'entropy'],'max_depth': range(1,30),#[1, 2, 3, 4, 5, 6, 7, 8,9,10],'max_leaf_nodes': [2,3,4, 5, 6, 7, 8, 9]  #最大叶节点数}dtree = tree.DecisionTreeClassifier()grid_search = GridSearchCV(dtree, parameters, scoring='accuracy', cv=5)grid_search.fit(x, y)print(grid_search.best_estimator_)  # 查看grid_search方法print(grid_search.best_score_) # 正确率print(grid_search.best_params_)  # 最佳 参数组合dtree = tree.DecisionTreeClassifier(criterion='gini', max_depth=3)dtree.fit(X_train, y_train)pred = dtree.predict(X_test)print(pred)print(y_test)print(classification_report(y_test, pred,target_names=['setosa', 'versicolor', 'virginica']))print(dtree.predict([[6.9,3.3,5.6,2.4]]))#预测属于哪个分类print(dtree.predict_proba([[6.9,3.3,5.6,2.4]]))  # 预测所属分类的概率值##print(iris.target)print(list(iris.target_names)) #输出目标值的元素名称#print(grid_search.estimator.score(y_test, pred))def irisdecisontree():from sklearn import datasetsiris = datasets.load_iris()X_train = iris.data[:,[0,1]][0:150]y_train = iris.target#print(iris.feature_names)#print(type(X_train))##print(X_train[:,[0,1]][0:150])clf = tree.DecisionTreeClassifier(max_depth=3,criterion='entropy')clf = clf.fit(X_train, y_train)with open("../output/iristree.dot",'w') as f:f = export_graphviz(clf,feature_names=['sepallength','sepalwidth'],out_file=f)import osos.system('dot -Tpdf "../output/iristree.dot" -o "../output/iristree.pdf"')def kfolddemo():'''1 shuffle=True结合random_state=整数 等效于shuffle=False 即出来的顺序不变2 验证集和训练集的比例大于1:8 小于1:2'''from numpy import arrayfrom sklearn.model_selection import KFold# data sampledata = array([0.1, 0.2, 0.3, 0.4, 0.5,0.6,0.7,0.8,0.9])# prepare cross validationkfold = KFold(n_splits=5, shuffle=True)# enumerate splitsfor train, test in kfold.split(data):print('train: %s, test: %s' % (data[train], data[test]))if __name__ == '__main__':##decisontreeSimple()##decisontreeTitanic()##irisdecisontree()irisdt()##kfolddemo()

运行结果：

"D:\Program Files\Python37\python.exe" "E:/Decision tree/decisiontree.py"
DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=3,
max_features=None, max_leaf_nodes=6,
min_impurity_decrease=0.0, min_impurity_split=None,
min_samples_leaf=1, min_samples_split=2,
min_weight_fraction_leaf=0.0, presort=False,
random_state=None, splitter='best')
0.9733333333333334
{'criterion': 'gini', 'max_depth': 3, 'max_leaf_nodes': 6}
[0 2 0 1 0 0 2 2 2 0 1 2 2 0 0 2 1 2 1 0 1 2 1 1 1 2 2 2 1 1]
[0 2 0 1 0 0 2 2 2 0 1 2 2 0 0 2 1 2 1 0 1 2 1 1 1 2 2 2 2 1]
precision recall f1-score support

setosa 1.00 1.00 1.00 8
versicolor 0.90 1.00 0.95 9
virginica 1.00 0.92 0.96 13

accuracy 0.97 30
macro avg 0.97 0.97 0.97 30
weighted avg 0.97 0.97 0.97 30

[2]
[[0. 0. 1.]]
['setosa', 'versicolor', 'virginica']

Process finished with exit code 0

决策树结合网格搜索交叉验证的例子相关推荐

sklearn学习-SVM例程总结3(网格搜索+交叉验证——寻找最优超参数)
网格搜索+交叉验证--寻找最优超参数 1548962898@qq.com 连续三天写了三篇博客,主要是为了尽快了解机器学习中算法以外的重要知识,这些知识可以迁移到每一个算法中,或许说,这些知识是学习并 ...
时间序列预测：SVR用于时间序列预测代码+模型保存+模型加载+网格搜索+交叉验证
本文关于SVR时间序列的预测,详细步骤如下: 1.数据读取 2.数据集的划分(采用滑动窗口重叠切片) 3.训练数据集掷乱 4.SVR参数设置(网格搜索+交叉验证) 5.SVR模型训练+模型保存 6.S ...
ML之DT：基于简单回归问题训练决策树(DIY数据集+七种{1~7}深度的决策树{依次进行10交叉验证})
ML之DT:基于简单回归问题训练决策树(DIY数据集+七种{1~7}深度的决策树{依次进行10交叉验证}) 目录输出结果设计思路核心代码输出结果设计思路核心代码 for iDepth in ...
《写给程序员的数据挖掘实践指南》——5.2. 10折交叉验证的例子
本节书摘来自异步社区出版社<写给程序员的数据挖掘实践指南>一书中的第5章,第5.2节,作者:[美]Ron Zacharski(扎哈尔斯基),更多章节内容可以访问云栖社区"异步社区 ...
Spark网格搜索——训练验证集拆分
前言 Spark内的网格搜索主要有两种评估流程,分别是交叉验证和训练验证集拆分,这篇文章主要介绍训练验证集拆分的具体流程数据集划分训练集.验证集.测试集训练集(train):训练模型验证 ...
2022-1-17第三章机器学习基础--网格搜索超参数优化、决策树、随机森林
交叉验证与网格搜索 ①交叉验证(训练集划分-训练集.验证集)–将所有数据分成n等分-并不具备调参能力 4等分就是4折交叉验证:一般采用10折交叉验证 ②网格搜索-调参数(与交叉验证一同使用) 如果有多 ...
ML：基于葡萄牙银行机构营销活动数据集(年龄/职业等)利用Pipeline框架(两种类型特征并行处理)+多种模型预测(分层抽样+调参交叉验证评估+网格/随机搜索+推理)客户是否购买该银行的产品二分类案
ML之pipeline:基于葡萄牙银行机构营销活动数据集(年龄/职业/婚姻/违约等)利用Pipeline框架(两种类型特征并行处理)+多种模型预测(分层抽样+调参交叉验证评估+网格搜索/随机搜索+模型 ...
交叉验证和超参数调整：如何优化您的机器学习模型
In the first two parts of this article I obtained and preprocessed Fitbit sleep data, split the data ...
【超参数寻优】交叉验证（Cross Validation）超参数寻优的python实现：多参数寻优
[超参数寻优]交叉验证(Cross Validation)超参数寻优的python实现:多参数寻优一.网格搜索原理二.网格搜索+交叉验证用于多参数寻优的python实现 1.训练模型及待寻优参数 ...

决策树结合网格搜索交叉验证的例子

决策树结合网格搜索交叉验证

决策树结合网格搜索交叉验证的例子相关推荐

最新文章

热门文章