clf=DecisionTreeClassifier()scores=cross_val_score(clf,X,y,scoring='accuracy')print(np.mean(scores)) 其中,clf=DecisionTreeClassifier表示分类器采用的决策树算法。 cross_val_score表示对自变量X和因变量y采用clf对应的算法,进行交叉验证。每一次都有一列真实值和预测值,两者进行对比算出这次训练的得分,依次...
#DecisionTreeClassifier:定义随机森林中的决策树分类器 #随机森林(Random Forest)的构建步骤如下: 准备训练数据集;重复步骤(随机选择特征子集;随机采样训练数据集;构建决策树;);对新样本进行预测;模型评估;特征重要性评估 #步骤概览: # 模型训练 #def fit(self, X, y): """ 训练随机森林模型 :param X: 训练...
dt_model = DecisionTreeClassifier(criterion='gini', max_depth=6, random_state=12) # 训练模型 dt_model.fit(X_train, y_train) #预测 dt_pred = dt_model.predict(X_test) print("对测试集300个数据的预测值为:") print(dt_pred) #决策树模型可视化 import matplotlib.pyplot as plt from sk...
# clf=tree.DecisionTreeClassifier()clf=tree.DecisionTreeClassifier(criterion='entropy')clf=clf.fit(dummyX,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)oneRow...
1# Fitting the Decision Tree to the Trainingset2fromsklearn.tree import DecisionTreeClassifier3classifier = DecisionTreeClassifier(criterion ='entropy', random_state =0)4classifier.fit(X_train, y_train) 5.8.对测试集进行分类: 代码如下:
x_train,x_test,y_train,y_test =train_test_split(data_train,data_target,test_size=0.2,random_state=24)fromsklearn.treeimportDecisionTreeClassifiermodel=DecisionTreeClassifier() model.fit(x_train,y_train) model.score(x_test,y_test),model.score(x_train,y_train) ...
from sklearn.tree import DecisionTreeClassifier as DTC dtc = DTC(criterion = "entropy") dtc.fit(x,y) 1. 2. 3. 4. 输出如下: 参数说明如下: class_weight:类别权重,可选参数,默认是None,也可以字典、字典列表、balanced。指定样本各类别的的权重,主要是为了防止训练集某些类别的样本过多,导致训练的决...
DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=2, max_features=None, max_leaf_nodes=None, 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, ...
# 创建决策树分类器对象clf = DecisionTreeClassifier() 5. 估计分类器预测结果的准确程度。准确度是通过比较实际测试集值和预测值来计算的。 # 模型准确率,分类器正确的概率是多少?print("准确率:",metrics.accuracy_score(y_test, y_pred)) 我们的决策树算法有67.53%的准确性。这么高的数值通常被认为是好的...