sklearn 数据切分

Bertha 。 2023-05-22 08:43 9阅读 0赞

sklearn.model_selection.train_test_split

param：
    *arrayssequence of indexables with same length / shape[0]
    #Allowed inputs are lists, numpy arrays, scipy-sparse matrices or pandas dataframes.
    #允许的输入是列表，numpy数组，稀疏矩阵或pandas数据。
    
    test_sizefloat, int or None, optional (default=None)
    #If float, should be between 0.0 and 1.0 and represent the proportion of the dataset to include in the test split. If int, represents the absolute number of test samples. If None, the value is set to the complement of the train size. If train_size is also None, it will be set to 0.25.
    #如果为float，则应在0.0到1.0之间，并表示要包含在火车分割中的数据集的比例。如果为int，则表示火车样本的绝对数量。如果为“无”，则该值将自动设置为测试大小的补码。
    
    train_sizefloat, int, or None, (default=None)
    #If float, should be between 0.0 and 1.0 and represent the proportion of the dataset to include in the train split. If int, represents the absolute number of train samples. If None, the value is automatically set to the complement of the test size.
    
    random_stateint, RandomState instance or None, optional (default=None)
    #If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.
    #如果为int，则random_state是随机数生成器使用的种子；否则为false。如果是RandomState实例，则random_state是随机数生成器；如果为None，则随机数生成器是所使用的RandomState实例np.random。
    
    shuffleboolean, optional (default=True)
    #Whether or not to shuffle the data before splitting. If shuffle=False then stratify must be None.
    #拆分前是否对数据进行混洗。如果shuffle = False，则分层必须为None。
    
    stratifyarray-like or None (default=None)
    #If not None, data is split in a stratified fashion, using this as the class labels.

例子：

import numpy as np
    from sklearn.model_selection import train_test_split
    X, y = np.arange(10).reshape((5, 2)), range(5)
    X
    array([[0, 1],
           [2, 3],
           [4, 5],
           [6, 7],
           [8, 9]])
    list(y)
    [0, 1, 2, 3, 4]

X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.33, random_state=42)
    X_train
    array([[4, 5],
           [0, 1],
           [6, 7]])
    y_train
    [2, 0, 3]
    X_test
    array([[2, 3],
           [8, 9]])
    y_test
    [1, 4]

train_test_split(y, shuffle=False)
    [[0, 1, 2], [3, 4]]