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A friend of mine answered on this question in different social media on different language, I'll post his answer here: 1. scaler should be saved in this case. You do fit_transform in the example the second time you run it, but you should just transform. Scaler should be "fit" once on data train and not change it afterwards. Then you will get 5 in ...


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Outside of using a generated dataset to study machine learning, the typical purpose of a trained machine learning model is to process new inputs from some source. For a model to be effective, the training data set inputs and new inputs should be taken from the same distribution. The loss function used in training, combined with cross-validation to measure ...


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Data augmentation is usually rotating, cropping and translating images. And this makes sense if your network could meet these kind of images. If I take a digit recognition like LeNet, it is useless to complicate the task of the network by forcing it to learn rotated digits, which could lead to a more complex architecture and training and less accuracy in the ...


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You can use stratified cross validation combined with an imbalanced learning technique applied to the training data. Stratification ensures that when you split your data into train and test, the ratio of frequencies between the classes will stay the same and therefore the test data will always be "realistic". However, when training a model (using ...


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