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How to train and evaluate a deep learning model for very imbalanced classification (e.g. classification with 99.82% of negatives and 0.18% of positives)? Here's an example using the Kaggle credit card fraud dataset.https://colab.research.google.com/drive/1xL2jSdY-MGlN60gGuSH_L30P7kxxwUfM …
In summary: set an appropriate `class_weight`, and use very large batches so that each batch contains at least a few positive samples. Make sure to monitor precision and recall during training.
Excellent tutorial and tips, Mr Chollet.
And focal loss?
The semantic is clear but what is the "class_weight" parameter doing inside the function 'fit' to balance the imbalance?
Thank you so much for this tutorial. Precisely, I am trying to do an image multilabel classification. The class with worst metrics, it is the one with fewer cases. Is there a similar way to approach the problem using multi hot encoding vectors?
Looks like val_precision jumps around a bit. Would this stabilize with more epochs or is this a feature of training an imbalanced dataset?
Thank you! This question comes up all the time.
What are the other questions that come up all the time? :)
Interesting case. I have a question about batch size. While I totally understand the idea of having a large size to have at least a few pos. samples, it also seems reducing batch size yields better results in general. So, aren't we also loosing some perf due to big batch size?
Interesting question, small batches tend to generalize better so perhaps artificially constructed (stochastic) minibatches in which at least some positive samples are included (stratified sampling) could result in good performance. Would be interesting to see some experiments.
Nice. Is it possible to demand "at least few positive examples" per batch explicitly using the Keras framework?
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