X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.4,random_state=42) X_train, X_test, y_train, y_test 现在 1). X_train - 这包括您所有的自变量,这些将用于训练模型,正如我们指定的 test_size = 0.4 ,这意味着 60% 将使用来自您完整数据的观察结果训练/拟合...
from sklearn.linear_model import Lasso # 假设 X_train 是特征数据,y_train 是目标变量 alpha = 0.1 # 正则化系数 lasso = Lasso(alpha=alpha) lasso.fit(X_train, y_train) # 选择系数不为零的特征 selected_features = [feature for feature, coef in zip(X_train.columns, lasso.coef_) if coef ...
load_linnerud() X, y = linnerud.data, linnerud.target # Split the dataset into training and testing sets X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=42) 这段代码将输入数据(X)和目标变量(y)分开,并使用scikit-learn datasets模块加载Linneru...
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=666) 3.1 理解数据 可以看到变量比较的多,先进行分类,除去目标变量label,此数据集的字段可以分成三个类别:订单相关指标、客户行为相关指标、酒店相关指标。 4 特征工程 # 用训练集进行数据探索 train = pd.conca...
knn.fit(X_train, y_train) 五、模型评估 在模型训练完成后,需要对其进行评估。我们将使用测试集来评估模型的准确性。 python 复制代码 from sklearn.metrics import accuracy_score, classification_report, confusion_matrix # 预测测试集结果 y_pred = knn.predict(X_test) ...
from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score data = load_breast_cancer() X = data.data y = data.target data.data.shape lrl1 = LR(penalty="l1",solver="liblinear",C=0.5,max_iter=1000) ...
#使用sklearn.cross_validation里的train_test_split模块用于分割数据 from sklearn.cross_validation import train_test_split #随机采样25%的数据用于测试,剩下的75%用于构建训练集合 X_train,X_test,y_train,y_test=train_test_split(data[column_names[1:10]],data[column_names[10]],test_size=0.25,rando...
pipe_lr.fit(X_train,y_train) print('Test Accuracy:%.3f'%pipe_lr.score(X_test,y_test)) Pipline对象采用元祖的序列作为输入,其中每个元祖中的第一个值为一个字符串,它可以是任意标识符,我们通过它来访问流水线中的元素,而元祖的第二个值则为scikit-learn中的一个转换器或者评估器 ...
支持向量机:svm_model = SVC(),svm_model.fit(X_train, y_train) 朴素贝叶斯:nb_model = GaussianNB(),nb_model.fit(X_train, y_train) K近邻分类:knn_model = KNeighborsClassifier(),knn_model.fit(X_train, y_train) 近邻回归:KNeighborsRegressor(n_neighbors=5).fit(X_train, y_train) ...
y=iris.target # 1)归一化前,将原始数据分割 fromsklearn.model_selectionimporttrain_test_split X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2, stratify=y,# 按照标签来分层采样 shuffle=True,# 是否先打乱数据的顺序再划分 ...