拆分用于分类问题的数据集的正确程序是什么？
高增
问题内容：
问题答案：
我是机器学习和深度学习的新手。我想澄清我与train_test_split训练之前有关的疑问
我有一个size的数据集(302, 100, 5)，其中，
(207,100,5) 属于 class 0
(95,100,5) 属于 class 1.
我想使用LSTM执行分类（因为序列数据）
由于各类没有相等的分布集，我如何拆分我的数据集进行训练？
选项1 ：考虑整个数据[(302,100, 5) - both classes (0 & 1)]，对其进行洗牌，train_test_split，然后进行培训。
选项2： 均等地分割两个班级数据集 [(95,100,5) - class 0 & (95,100,5) - class 1]，对其进行洗牌，train_test_split，然后进行训练。
在训练之前进行分割的更好方法是什么，以便我在减少损失，准确性，预测等方面可以获得更好的结果？
如果除了上述2个选项以外，还有其他选择，请推荐，
根据评论部分，我包含了部分数据：
X_train：形状（241 * 100 * 5）
每100 * 5中的每一行对应1个时间步长最后100行对应于100个时间步长（以毫秒为单位）
array([[[0.98620635, 0. , 0.12752912, 0.60897341, 0.46903766],
[0.97345112, 0. , 0.12752912, 0.49205995, 0.38709902],
[0.9566397 , 0. , 0.12752912, 0.45728718, 0.42154812],
...,
[0.28669754, 0.8852459 , 0.12752912, 0.8786213 , 0.80125523],
[0.31559784, 0.8852459 , 0.20968731, 0.89087803, 0.79476987],
[0.34368841, 0.8852459 , 0.12752912, 0.89087803, 0.71066946]],
[[0.97957188, 0.14909194, 0.04159147, 0.50548561, 0.34209531],
[0.9687237 , 0.13964397, 0.04159147, 0.55926067, 0.64613533],
[0.96596236, 0.13553813, 0.04159147, 0.55903796, 0.85299319],
...,
[0.49309139, 0.72396527, 0.04159147, 0.81998825, 0.12362443],
[0.52072591, 0.70872926, 0.04159147, 0.82361951, 0.89639432],
[0.54441507, 0.71835207, 0.04159147, 0.84964602, 1. ]],
[[0.48151381, 0.875 , 0.16666667, 0.90637286, 0.62737926],
[0.53325374, 0.8625 , 0.33333333, 0.87881677, 0.5321154 ],
[0.57506452, 0.81859091, 0.16666667, 0.84915758, 0.3552661 ],
...,
[0.34456041, 0.92993213, 0.33333333, 0.92953899, 0.78782408],
[0.39496018, 0.90523485, 0.33333333, 0.9117954 , 0.54579383],
[0.44187985, 0.8625 , 0.33333333, 0.84163194, 0.25789356]],
...,
[[0.16368355, 0. , 0.15313225, 0.40101906, 0.36784741],
[0.15679684, 0. , 0.15313225, 0.4435126 , 0.67351994],
[0.15544309, 0.06132052, 0.15313225, 0.40101906, 0.36611345],
...,
[0.43936628, 0.68292683, 0.15313225, 0.82305329, 0.36784741],
[0.49751546, 0.68292683, 0.07764888, 0.84141109, 0.42828833],
[0.53288488, 0.68292683, 0.15313225, 0.85959823, 0.36784741]],
[[0.9418247 , 0.30821318, 0.03072816, 0.744977 , 0.93769733],
[0.9537216 , 0.28989357, 0.03072816, 0.74576381, 0.98468743],
[0.96455286, 0.21736423, 0.03072816, 0.74182977, 1. ],
...,
[0.36273884, 0.60113245, 0.06145633, 0.85409181, 0.32277415],
[0.38774614, 0.57789971, 0.05844559, 0.82937631, 0. ],
[0.41546859, 0.57789971, 0.03072816, 0.79315883, 0.31256578]],
[[0.97868688, 0.06451613, 0.00411829, 0.64705259, 0.69827586],
[0.97999663, 0.06451613, 0.02256676, 0.66812232, 0.75195925],
[0.97143037, 0.02476377, 0.02256676, 0.66317859, 0.78487461],
...,
[0.50336862, 0.73867709, 0.02256676, 0.84921606, 0.1226489 ],
[0.54003486, 0.72043011, 0.02256676, 0.82679269, 0.20297806],
[0.57594039, 0.70967742, 0.02256676, 0.83350205, 0. ]]])
Y_train：形状（241，）
[1. 0. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 0. 0. 1. 1. 1. 0. 0. 1. 1. 0. 0. 0.
1. 1. 1. 0. 1. 0. 0. 1. 0. 0. 0. 0. 0. 0. 1. 0. 1. 0. 0. 0. 0. 0. 1. 1.
0. 0. 1. 0. 0. 0. 0. 1. 0. 1. 0. 0. 0. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 0.
0. 1. 0. 0. 1. 1. 0. 0. 0. 1. 1. 0. 0. 1. 0. 0. 1. 0. 0. 1. 0. 0. 0. 1.
1. 0. 0. 1. 0. 1. 0. 0. 0. 1. 1. 0. 0. 0. 0. 1. 1. 1. 0. 0. 0. 0. 0. 1.
0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 1. 1. 0. 0. 0. 0. 0. 0. 1. 0. 0. 1. 1. 0. 1. 1. 0. 0. 0. 0. 0. 1. 0.
0. 0. 0. 0. 0. 0. 0. 1. 0. 1. 0. 1. 0. 0. 0. 1. 1. 0. 0. 1. 1. 1. 0. 1.
0. 1. 0. 1. 0. 0. 0. 1. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.
1. 0. 0. 1. 1. 1. 0. 1. 0. 0. 1. 0. 1. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 1.
1.]
供参考
，如您在上面看到的，X火车数据很大，我不能包含我的整个X_train数据的完整集合。因此，这里我仅提供数据的一个细分，以更好地了解1个细分的数据(i.e X_train[0] : shape- (100*5))。其余的240或多或少如下图所示
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[2.32490138e-01, 8.85245902e-01, 1.27529123e-01, 7.59549923e-01,
8.41851137e-01, 9.52301255e-01],
[2.58952796e-01, 8.85245902e-01, 1.27529123e-01, 6.97804020e-01,
8.55667806e-01, 8.68200837e-01],
[2.86697538e-01, 8.85245902e-01, 1.27529123e-01, 6.25149288e-01,
8.78621304e-01, 8.01255230e-01],
[3.15597842e-01, 8.85245902e-01, 2.09687308e-01, 5.51940700e-01,
8.90878027e-01, 7.94769874e-01],
[3.43688409e-01, 8.85245902e-01, 1.27529123e-01, 4.75801089e-01,
8.90878027e-01, 7.10669456e-01]])
问题答案：
TLDR： 两者都尝试！
在数据集不平衡之前，我曾遇到过类似情况。我使用train_test_split或KFold通过。
但是，一旦我偶然发现了处理不平衡数据集的问题，便遇到了过度平衡和欠平衡的技术。为此，我建议使用库：imblearn
您将在其中找到各种技巧来处理其中一个类别的人数超过另一个类别的情况。我个人经常使用SMOTE，并且在这种情况下取得了相对较好的成功。
其他参考：
https://www.analyticsvidhya.com/blog/2017/03/imbalanced-classification-
problem/
https://towardsdatascience.com/handling-imbalanced-datasets-in-machine-
learning-7a0e84220f28
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