sklearn实现:train_test_split(X, y, test_size, random_state) X: 特征矩阵 y: 目标向量 test_size: 检验集大小(比例),通常选择0.3,0.25,0.2等 random_state: Numpy RandomState对象或代表随机数种子的整数,由于划分是随机的,为了重复实验过程,应该使用统一的随机数种子。 使用IRIS数据集。 importnumpyasnpimp...
train_test_split()是sklearn.model_selection中的分离器函数,⽤于将数组或矩阵划分为训练集和测试集,函数样式为: X_train, X_test, y_train, y_test = train_test_split(train_data, train_target, test_size, random_state,shuffle) 参数解释: train_data:待划分的样本数据 train_target:待划分的样本数...
X_train,X_test, y_train, y_test =sklearn.model_selection.train_test_split(train_data,train_target,test_size=0.4, random_state=0,stratify=y_train) # train_data:所要划分的样本特征集 # train_target:所要划分的样本结果 # test_size:样本占比,如果是整数的话就是样本的数量 # random_state:是...
首先，我们把目标Item_Outlet_Sales存储到sales变量，把test_Item_Identifier和test_Outlet_Identifier存储到id变量。然后，组合训练集和测试集，这样省去两次执行相同步骤的麻烦。combi = train.append(test, ignore_index=True)接着，检查数据集中的缺失值。combi.isnull().sum()变量Item_Weight和Outlet_size中有相...
test_size:可以接收float,int或者None。如果是float,则需要传入0.0-1.0之间的数,代表测试集占总样本数的比例。如果传入的是int,则代表测试集样本数,如果是None,即未声明test_size参数,则默认为train_size的补数。如果train_size也是None(即两者都是None),则默认是0.25。
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1) # Train a random forest model rf = RandomForestClassifier(n_estimators=100, random_state=1) rf.fit(X_train, y_train) # Get baseline accuracy on test data ...
iris.target,test_size=0.4,random_state=0)>>>scaler=preprocessing.StandardScaler().fit(X_train)>>>X_train_transformed=scaler.transform(X_train)>>>clf=svm.SVC(C=1).fit(X_train_transformed,y_train)>>>X_test_transformed=scaler.transform(X_test)>>>clf.score(X_test_transformed,y_test)...
1. train_test_split(under_x, under_y, test_size=0.3, random_state=0) # under_x, under_y 表示输入数据, test_size表示切分的训练集和测试集的比例, random_state 随机种子 2. KFold(len(train_x), 5, shuffle=False) # len(train_x) 第一个参数数据数据大小, 5表示切分的个数,即循环的次数...
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # 标准化数据 scaler = StandardScaler() X_train = scaler.fit_transform(X_train) X_test = scaler.transform(X_test) 四、训练机器学习模型 ...
target # 划分数据集为训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # 创建KNN分类器,并设置邻居数量为3 knn = KNeighborsClassifier(n_neighbors=3) # 使用训练数据训练KNN分类器 knn.fit(X_train, y_train) # 使用测试数据进行...