python - Neural network classifier using MLP -

i'm new machine learning , i'm working on python application classifies poker hands using dataset i post snippets. not seem work well. cannot classify hands correctly. , getting following error

", line 298, in fit     raise valueerror("multioutput target data not supported " valueerror: multioutput target data not supported label binarization 

the following code:

import pandas pnd import numpy np sklearn.model_selection import train_test_split sklearn.preprocessing import standardscaler sklearn.neural_network import mlpclassifier sklearn.metrics import classification_report training = pnd.read_csv(".idea/train.csv") training.keys() training.shape x = np.array(training) y = np.array(training) x_train, x_test, y_train, y_test = train_test_split(x, y) scaler = standardscaler() # fit training data scaler.fit(x_train) x_train = scaler.transform(x_train) x_test = scaler.transform(x_test) mlp = mlpclassifier(hidden_layer_sizes=(30, 30, 30, 30, 30, 30, 30, 30, 30, 30)) mlp.fit(x_train, y_train) predictions = mlp.predict(x_test) print(classification_report(y_test, predictions)) len(mlp.coefs_) len(mlp.coefs_[0]) len(mlp.intercepts_[0]) 

the following sample of data set using: image here

and here desciption of data set: https://archive.ics.uci.edu/ml/datasets/poker+hand

is there wrong? hope guide me if i'm doing things right way.

just keep answer here.

the problem scalet.fit has contain y_train.

change:

scaler.fit(x_train) 

to:

scaler.fit(x_train, y_train)