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I am using MLPClassifier. The model consists of 3 hidden layers each of 128 neurons. The input dataset has 1 million points each of dim = 128. After playing around MLPClassifier parameters, I still get poor prediction accuracy. I am not sure if this is caused by underfitting or not! Can underfitting be caused by the lack of enough data during the training process?

Thank you

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Did you compare train/validation loss? If both are large it could be underfitting. Although, 1 million points seem like a lot of data.

To make sure that your code is correct I would suggest to overfit the model - pick a small part of your dataset and see if train loss goes to zero (and accuracy goes to 1).

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