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...
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...
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。 train_size:和前者同理。 random_state:可...
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...
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 ...
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 ...
dataset=loadtxt('pima-indians-diabetes.csv',delimiter=",")# split data intoXand yX=dataset[:,0:8]Y=dataset[:,8]# split data into train and test sets X_train,X_test,y_train,y_test=train_test_split(X,Y,test_size=0.33,random_state=7)# fit model no training data ...
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...
from kivy.uix.buttonimportButtonclassTestApp(App):defbuild(self):returnButton(text=" Hello Kivy World ")TestApp().run() 结果如下。 04. wxPython wxPython是一个跨平台GUI的Python库,可轻松创建功能强大稳定的GUI,毕竟是用C++编写的～ 目前,支持Windows,Mac OS X,macOS和Linux。
= train_test_split( iris['data'], iris['target'], test_size=0.2 ) print( 'The size of X_train is ', X_train.shape ) print( 'The size of y_train is ', y_train.shape ) print( 'The size of X_test is ', X_test.shape ) ...