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No.
What you refer to (the difference between the means of group A and B) is actually the effect size (sometimes called size effect), and it has absolutely nothing to do with the p-values.
The situation is nicely summarized in the (highly recommended) paper Using Effect Size—or Why the P Value Is Not Enough (emphasis mine):
Why Report Effect Sizes?
The ...
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This is an interesting question. So sorry for a long winded answer. The tl:dr; is it is a mix of some real applicability, theoretical basis, historical baggage (due to limited compute power) and obsession for analytically tractable models (instead of simulation/computational models). We should be very careful and discerning while using it in real problems.
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Well, keep in mind that when you standardize/impute data you're estimating parameters. Given the conditions that you've defined and having enough data such that the estimates are good, then I don't think it should matter to use the training data or all the data (as a matter of fact, the estimate of the parameter using training data should be very similar to ...
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The answer to this question really depends on the insights you are after with these statistics.
Generally speaking however, it is wise to investigate per class in such a situation. Taking your examples this would entail comparing average string lengths for both classes. This will help you understand how both classes are (dis)similar given the dataset.
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There are some distance/dissimilarity functions for two binary/boolean vectors such as Jaccard distance and Hamming distance. You can check them from scipy's documentation. You can calculate similarities between two boolean columns by using these distance functions.
from scipy.spatial import distance
distance.hamming([1, 0, 0], [0, 1, 0])
0....
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I think there might be a bit of confusion here: what is usually called a stable model is a model for which the performance doesn't vary (or not significantly) when sampling a different subset of training data or test data. In other words a model is stable if chance doesn't affect its performance. Typically one can use cross-validation in order to assess the ...
1
The learning rate is one of those first and most important parameters of a model, and one that you need to start thinking about pretty much immediately upon starting to build a model. It controls how big the jumps your model makes, and from there, how quickly it learns.
There are learning rate technique called Cyclical Learning Rates.
Training with cyclical ...
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