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I have the text classification problem. Dataset is imbalanced in terms of classes. I'm using StratifiedKFold and balanced weights updating during training LogisticRegression. Let's say my score is:

  • 90% train
  • 60% validation

obviously, I need to address the overfitting, adding the regularization to the model. I would suspect that adding regularization will give something like:

  • 80% train
  • 65% validation

What I don't understand is that adding more regularization worsens validation score:

  • 80% train
  • 58% validation

On the other hand, reducing regularization gives:

  • 98% train
  • 62% validation

In theory, more regularization worsens train score but can help with the validation score. What may be the source of such behavior of the model?

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  • $\begingroup$ 58 60 62 seems pretty close for me, especially when, as you say, class distribution is imbalanced. So my guess is that there is a much bigger problem hidden there than regularization: 1) how much testing example do you have ? 2) what is the proportion of the largest class ? May be in all your experiments, your model is overfitting, and there is no real difference in terms of generalization between your 3 models. $\endgroup$
    – xtof54
    Aug 9 '18 at 19:43
  • $\begingroup$ Thank you. I have 5k examples, I'm using Stratified cross-validation with 5 folds. There are 50 classes, some have ~25 examples, some ~200, one is much bigger - 600 $\endgroup$
    – DavidS1992
    Aug 10 '18 at 7:54
  • $\begingroup$ have you tried using class_wieghts or sample_weights? $\endgroup$ Aug 11 '18 at 6:24
  • $\begingroup$ Yes I'm using class_wieghts='balanced' in scikit $\endgroup$
    – DavidS1992
    Aug 12 '18 at 12:47
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In your case, the Wald confidence interval gives about +/-1.36% on the evaluation set. So I'm not so sure whether you may really conclude that regularization negatively impacts your accuracy. I think all we can say is that it does not improve generalization. Why ? I'm sorry, but I don't know, given the information provided: many things may go wrong. For instance, I have the feeling that 50 classes is a lot for a total of 5k samples; may be try and analyze the accuracy per class, to get an idea about what's going on.

Good luck !

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    $\begingroup$ Rather than accuracy, precision-recall would be a better choice I think $\endgroup$ Aug 11 '18 at 6:23

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