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我是机器学习新手，我正在研究一个使用数据集来分类扑克手的python应用程序，我将发布片段。它似乎不工作。而且我收到以下错误：MLP分类拟合
Traceback (most recent call last):
File "C:\Users\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 2881, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-62-0d21cd839ce4>", line 1, in <module>
mlp.fit(X_test, y_train.values.reshape(len(y_train), 1))
File "C:\Users\Anaconda3\lib\site-packages\sklearn\neural_network\multilayer_perceptron.py", line 618, in fit
return self._fit(X, y, incremental=False)
File "C:\Users\Anaconda3\lib\site-packages\sklearn\neural_network\multilayer_perceptron.py", line 330, in _fit
X, y = self._validate_input(X, y, incremental)
File "C:\Users\Anaconda3\lib\site-packages\sklearn\neural_network\multilayer_perceptron.py", line 902, in _validate_input
multi_output=True)
File "C:\Users\Anaconda3\lib\site-packages\sklearn\utils\validation.py", line 531, in check_X_y
check_consistent_length(X, y)
File "C:\Users\Anaconda3\lib\site-packages\sklearn\utils\validation.py", line 181, in check_consistent_length
" samples: %r" % [int(l) for l in lengths])
ValueError: Found input variables with inconsistent numbers of samples: [6253, 18757]
这里是我想产生的代码：
import pandas as pnd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.neural_network import MLPClassifier
from sklearn.metrics import classification_report, confusion_matrix
training_data = pnd.read_csv("train.csv")
training_data['id'] = range(1, len(training_data) + 1) # For 1-base index
training_datafile = training_data
target = training_datafile['hand']
data = training_datafile.drop(['id', 'hand'], axis=1)
X = data
y = target
X_train, X_test, y_train, y_test = train_test_split(X, y)
X_train.shape
y_train.shape
scaler = StandardScaler()
scaler.fit(X_train)
X_train = scaler.transform(X_train)
X_test = scaler.transform(X_test)
mlp = MLPClassifier(hidden_layer_sizes=(100, 100, 100))
mlp.fit(X_test, y_train.values.reshape(len(y_train), 1))
predictions = mlp.predict(X_test)
len(mlp.coefs_)
len(mlp.coefs_[0])
len(mlp.intercepts_[0])
print(confusion_matrix(y_test, predictions))
print(classification_report(y_test, predictions))
X_train.shape的形状（18757，10）和y_train的形状。形状（18757） 我已经尝试使用以下以前的帖子
y_train.values.reshape(len(y_train), 1)
但我仍然得到同样的错误。因为我不确定形状有什么问题，所以一些指导会有很大的帮助。
数据片段： 