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In order to fix overfitting, you can try the following things: 1.) Cross validation 2.) Get more data (won't work everytime) 3.) Remove redundant features 4.) Early stopping rounds if you are using GBM or DL 5.) Regularization (for example Ridge or Lasso in the case of Linear Regression) 6.) Perform extensive Feature engineering


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All GridSearch does is it looks for the best performing model among the parameters you have supplied it with. It won't fix overfitting for you. Overfitting happens when the model is to well adjusted to the training data. In case of SVM the model with C=1000 would definitely overfit and that is why it was not the best one. C=0.1 would probably underfit and ...


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Hard to tell. Usually you would expect some difference between the two, and you would worry if they have dissimilar shapes. But yours are very similar, and the validation curve has a smaller loss from the start, compared to the training loss. Maybe the training/validation split was just unfortunate. Try to train the model again with a new validation sample, ...


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It looks like overfitting. Check this article to learn about interpreting different types of learning curves. TensorFlow also has a tutorial on this topic. There is a clear split between the curves at about epoch 10 where training keeps learning at a much faster rate compared to validation. But as you point out validation loss stays pretty much stable with ...


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Overfitting is a meaningful drop in performance between training and prediction. Any model can overfit. Online DQN model could continue with data over time but not make useful predictions.


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As mentioned in the comments, check the training and test/validation metrics and compare them. If the training metric is much better than the test/validation metric, then there are high chances you are overfitting. Another way to check for overfitting is to plot learning curves. They are basically curves for model performance. Check out the sklearn page for ...


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Imho the most likely explanation is that the submission test set doesn't follow the same distribution as the training/validation/test data that you used to train and evaluate the model. In other words the test data that they use to evaluate is not a random sample from the full data, it's a different dataset collected independently, for example at a different ...


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