X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # 进行自定义变换 ct = CustomTransformer() X_train_new = ct.fit_transform(X_train) X_test_new = ct.transform(X_test) # 使用线性回归模
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # 创建决策树分类器 clf = DecisionTreeClassifier(criterion='entropy') # 使用信息增益作为划分标准 # 训练模型 clf.fit(X_train, y_train) # 预测测试集 y_pred = clf.predict(X_test) # 计算...
train_x=["Pclass","Sex","SibSp","Parch", "Embarked","Age_band","re"] ##将训练集切分为训练集和验证集 X_train,X_val,y_train,y_val=train_test_split( data[train_x],data[Target], test_size=0.25,random_state=1) ##先使用默认的参数建立一个决策树模型 dtc1=DecisionTreeClassifier(ran...
1、Numpy 2、Pandas 3、Matplotlib 4、Seaborn 5、Pyecharts 6、wordcloud 7、Faker 8、PySimpleGUI ...
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # 创建随机森林模型 rf_model = RandomForestClassifier(n_estimators=100, random_state=42) # 训练模型 rf_model.fit(X_train, y_train) # 进行预测 y_pred = rf_model.predict(X_test) # 计算...
'Loan_Amount']]=scaler.fit_transform(data[['Annual_Income','Credit_Score','Years_in_Job','Loan_Amount']])# 分割数据X=data[['Annual_Income','Credit_Score','Years_in_Job','Loan_Amount']]y=data['Loan_Status']X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,...
from sklearn.model_selection import train_test_split,GridSearchCV from sklearn.svm import OneClassSVM from pyclustering.cluster.kmedians import kmedians from pyclustering.cluster.fcm import fcm from pyclustering.cluster import cluster_visualizer ...
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) CART模型构建 使用DecisionTreeClassifier从sklearn.tree库中进行CART模型的构建和训练。 from sklearn.tree import DecisionTreeClassifier # 创建CART分类模型 ...
X_train, X_test, y_train, y_test = train_test_split(data, labels, test_size=0.2, random_state=42) 步骤3: 特征提取 使用词袋模型提取文本特征: vectorizer = CountVectorizer() X_train_vectorized = vectorizer.fit_transform(X_train) X_test_vectorized = vectorizer.transform(X_test) ...
clf = clf.fit(X_train,y_train) #用训练集数据训练模型 result = clf.score(X_test,y_test) #对我们训练的模型精度进行打分 1. 2. 3. 4. 分类树 DecisionTreeClassifier class sklearn.tree.DecisionTreeClassifier ( criterion=’gini’, splitter=’best’, max_depth=None,min_samples_split=2, min...