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I'm building a binary classification model using a neural network, with python and the libraries tensorflow and keras.

For that I have an unequal amount of labeled data: Around 2'000'000 labeled with 1 and only 25'000 with 0.

In order to train the model meaningfully, a balanced amount of data would be advantageous (as far as I understand).

However, I would also like to run the model with different ratios of the labeled data (3/1, 1/1, 1/3) in order to gain insights there too.

How is that possible? First of all to balance the imbalanced dataset, and then run the model with a given ratio (3/1, 1/1, 1/3) of labeled data.

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Instead of subsampling the majority class, you can simply use class weights to give more importance to the minority class samples. For this, you can simply pass a dictionary with the weights to model.fit:

class_weight = {0: 1., 1: 50.}
model.fit(X_train, Y_train, nb_epoch=10, batch_size=255, class_weight=class_weight)

This way, instead of having different subsampling rations (75/25, 50/50, 25/75), you can train with different values of class_weight and evaluate their impact on the end result.

I suggest you check out the official Tensorflow tutorial on imbalanced data, where they discuss this approach.

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  • $\begingroup$ Thanks for this hint. I followed the instructions on TensorFlow tutorial about imbalanced datas. The class_weights are now set, but the output of the confusions matrix is very one-sided, 99% are in the confusions matrix in the positive/positive field, and 1% distributed on the other fields. Accuracy (0.98) and f1-score also. But it looks like something is wrong with the confusions matrix. How can I fix this? $\endgroup$
    – user155518
    Feb 2 at 6:08
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    $\begingroup$ Please, create a new question with the source code so that the community and I can help with that. $\endgroup$
    – noe
    Feb 2 at 7:04

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