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It sometimes happens that a classification model will output probability estimates that are all in the low range. That means the model does not make any predictions that it is very sure are the positive class. Since only 10% of your data falls into the positive class, it appears to be a difficult problem to predict using your model. Do not transform the ...


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Shuffling data would not seem to make sense here, since your model has "memory". You're not predicting $y_i$ from only $x_i$, but also $x_{i-1}$ and $x_{i-2}$. If you shuffle the data and perform prediction, you are implying that $x_1, x_2, x_3$ should give the same value as $x_2, x_1, x_3$ or $x_9, x_5, x_3$, or any series of values that merely ends in $x_3$...


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The error is self-explanatory. You provide the model with only 3 features whereas it needs 12 features. In model.py you select 3 features from the dataset, indeed. However, you apply one-hot encoding that creates new columns. Each new column describes only one category and contains values 0 and 1: whether this category is observed in a sample or not. And the ...


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I believe it will be difficult to answer this question w/o knowing the underlying data. Let's suppose, N1 is from men's football and N2 is from women's football history then both should be treated as separate data entity or should be mixed to create train/test set if we have a compelling need. What I will suggest - Check the Mean, Max, Min, ...


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I understood it wrong ,here is the paper which discuss using multiple data set for the same classifier- http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.141.3142&rep=rep1&type=pdf They conclude- " We theoretically and empirically analyzed three families of statistical tests that can be used for comparing two or more classifiers over ...


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