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Oversampling on your test set will only artifically improve your performance. What you may want to do instead is changing your objective function to give more importance to you imbalanced class. There are already a lot of question about class imbalance on this website, such as : Classification problem: custom minimization measure


One way to estimate the level of confidence we have about an ANN prediction is to use dropout perturbations. The idea was proposed in this paper: Dropout as a Bayesian Approximation. Representing Model Uncertainty in Deep Learning. The core idea is to use dropout as a perturbation method, and check how predictions change with varying levels of dropout. Once ...


Your MAE_val is: MAE_val= np.mean(np.absolute(y_val - pred_val )) On the other side, you fit your model on: history =, y_train, ... So you are calculating them on different objects. Training data on one side, and validation set for a final evaluation.


Yes that's correct, but assuming that you follow the exact same methodology you will obtain exactly the same performance at the end, so there's no advantage. Keep in mind that the problem with class imbalance is not that one class is harder to identify than the other, but rather that it's harder to properly separate the two classes. [edit] It would be a ...


Yes, you can do that. However, how accurate your new labels are depends on the ability of your model to generalize to new data and the similarity of the new data to your training data. Therefore, it is something you need to test for in the first place by using a separate test dataset to assess model performance. In contrast to that, a validation dataset is ...


Question 1. Both. If you think in opposite to multivariate terms, than in univariate regression both input and output variables should be 1-d Question 2. Multivariate regression where more than one independent variable (predictors) and more than one dependent variable (responses), are linearly related. So input needs to be more than 2 also.

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