关于数据集的处理train-test集的分离

最新推荐文章于 2023-01-12 11:09:25 发布

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最新推荐文章于 2023-01-12 11:09:25 发布 · 1.5k 阅读

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本文介绍了如何使用scikit-learn库中的train_test_split函数进行数据集的train-test分离，详细解析了参数如test_size、train_size、random_state、shuffle和stratify的含义及用法。此外，还提及了自定义编写数据集分离的方法，提供了一个例子，其中训练集占75%，包含不同label数量的数据。

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方法一：train/test函数

参考官网：https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html#sklearn.model_selection.train_test_split

>>> 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]]

Parameters:
*arrays : sequence of indexables with same length / shape[0]
Allowed inputs are lists, numpy arrays, scipy-sparse matrices or pandas dataframes.

test_size : float, int or None, optional (default=None)
If float, should be between 0.0 and 1.0 and represent the proportion of the