clf = DecisionTreeClassifier(random_state=42) clf.fit(X_train, y_train) ``` 5.使用训练好的模型进行预测: ```python y_pred = clf.predict(X_test) ``` 6.评估模型性能: ```python accuracy = accuracy_score(y_test, y_pred) print("Accuracy:", accuracy) ``` 以上示例展示了如何使用`Deci...
import pandas as pd import numpy as np from sklearn.datasets import load_breast_cancer from sklearn.model_selection import train_test_split cancer=load_breast_cancer() x_train,x_test,y_train,y_test=train_test_split(cancer.data,cancer.target,stratify=cancer,random_state=42) tree=DecisionTree...
假设我们用target列作为标签。 X=data.drop('target',axis=1)# 特征集,去掉标签列y=data['target']# 标签集# 分割数据,70%训练集,30%测试集X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.3,random_state=42) 1. 2. 3. 4. 5. 步骤4:创建决策树分类器 现在我们可以创建一个...
开发者ID:wargile,项目名称:ML1,代码行数:16,代码来源:s1-8.py # 需要导入模块: from sklearn.tree import DecisionTreeClassifier [as 别名]# 或者: from sklearn.tree.DecisionTreeClassifier importrandom_state[as 别名]importpandasimportreimportnumpyasnpfromsklearn.treeimportDecisionTreeClassifier data =...
feature_train, feature_test, target_train, target_test = train_test_split(iris_feature, iris_target, test_size=0.33, random_state=42) 模型训练及预测 fromsklearn.treeimportDecisionTreeClassifier dt_model = DecisionTreeClassifier()# 所有参数均置为默认状态dt_model.fit(feature_train,target_train)#...
cross_validation.train_test_split(word_data, authors, test_size=0.1, random_state=42) vectorizer = TfidfVectorizer(sublinear_tf=True, max_df=0.5, stop_words='english') features_train = vectorizer.fit_transform(features_train) features_test = vectorizer.transform(features_test).toarray() ...
创建 DecisionTreeClassifier 实例 clf = DecisionTreeClassifier(random_state=42) # 4. 训练模型 clf.fit(X_train, y_train) # 5. 验证模型 predictions = clf.predict(X_test) accuracy = np.mean(predictions == y_test) print(f"Model accuracy: {accuracy:.2f}") 在这个示例中,我们首先加载了 ...
X_train,x_test,Y_train,y_test = train_test_split(cancer.data,cancer.target,test_size=0.2,random_state=3) 1. 2. 3. 4. 5. 6. 7. 8. 先把数据分成测试集和训练集,这里的训练集之后还会再被分成训练集+验证集。 下面随便用一组超参数构建lightGBM模型: ...
random_state :当将参数splitter设置为‘random’时,可以通过该参数设置随机种子号,默认为None,表示使用np.random产生的随机种子号。 max_leaf_nodes:设置决策树的最大叶子节点个数,该参数与max_depth等参数参数一起,限制决策树的复杂度,默认为None,表示不加限制。
1、R中重复值的处理 unique函数作用：把数据结构中，行相同的数据去除。 #导入CSV数据 data <- read....