Using MLPClassifier to build neural network for predicting multiple outputs

Question

I am implementing MLPClassifier to try a neural network now, but I am not sure if I can put multiple classes as an output. Here is my output, as follows:

y = array([1, 1, 2, 2, 1, 1, 2, 2, 2, 3, 3, 1, 1, 1], dtype=int64)

What I am trying to do is that I create multiple classes (1,2,3) and want to check if I can predict by using a neural network.

I am not sure if I am on the right path.

Can I only put a single binary output, such [0,1] or can I put multiple outputs that neural network can predict?

X_train, X_test, y_train, y_test = train_test_split(X, y,test_size=0.70, random_state=0)
mlp = MLPClassifier(solver='lbfgs',random_state=0, hidden_layer_sizes[1],alpha=0.001,max_iter=100)
mlp.fit(X_train, y_train)
y_pred=mlp.predict(X_test)

Keashav Nair · Accepted Answer · 2019-06-10 02:13:03Z

0

Why don't you try printing out y_pred to show if the classifier is performing a multi-class classification or binary classification? I think it will perform a multi-class classification.

answered Jun 10, 2019 at 2:13

Keashav Nair

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1 Comment

Juho Lee Over a year ago

y_pred array([1, 2, 1, 1, 2, 1, 1, 1, 2, 1], dtype=int64) It does predict! but I am not sure if it is a right way to do it...

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