我目前正在尝试训练在sklearn中实现的MLPClassifier ...
当我尝试使用给定值训练它时，出现此错误：
ValueError：使用序列设置数组元素。
feature_vector的格式为
[[one_hot_encoded品牌名称]，[不同的应用程序缩放为均值0和方差1]]
有人知道我在做什么错吗？
谢谢！
feature_vectors：
[
array（[0.，0.，0.，0.，0.，0.，0.，0.，0.，0.，0.，0.，0.，
0.，0.，0.，0.，0.，0.，0.，0.，0.，0.，0.，0.，0.，
0.，0.，0.，0.，0.，0.，0.，0.，0.，0.，0.，0.，0.，
0.，0.，0.，0.，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.，0.，0.，0.，0.，0.，0.，0.，0.，0.，0.，
0.，0.，0.，0.，0.，0.，0.，0.，0.，0.，0.]]），
数组（[0.82211852，-0.22976818，-0.22976818，-0.22976818，-0.22976818，
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-0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818，
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-0.22976818、0.3439882，-0.22976818，-0.22976818，-0.22976818，
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1.10899671，-0.22976818，-0.22976818，-0.22976818，-0.22976818，
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-0.22976818，-0.22976818，-0.22976818，-0.22976818、7.13343874，
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-0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818，
3.02151799、4.26465682，-0.22976818，-0.22976818，-0.22976818，
-0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818，
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-0.22976818，2.25650948，-0.22976818，-0.22976818，-0.22976818，
-0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818，
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-0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818，
-0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818，
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-0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818，
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-0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818，
-0.22976818，-0.22976818，-0.22976818，-0.22976818，0.3439882，
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-0.22976818，-0.22976818，-0.22976818，-0.22976818、3.49964831，
-0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818]）
]
g_a_group：
[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]
MLP：
从sklearn.neural_network导入MLPClassifier
clf = MLPClassifier（solver ='lbfgs'，alpha = 1e-5，
hidden_layer_sizes = [5，2），random_state = 1）
clf.fit（feature_vectors，g_a_group）
最佳答案
从scikit-learn的角度来看，您的数据对.fit调用的预期毫无意义。特征向量应该是大小为N x d的矩阵，其中N-数据点数和d特征数，第二个变量应包含标签，因此，其长度应为N（或N x k，其中k是每点的输出/标签数）。无论变量中所表示的是什么-它们的大小都与它们应表示的大小不匹配。