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Stratified shuffling used in the case target variable takes discrete values. "The folds are made by preserving the percentage of samples for each class," stated in sklearn documentation. So your question is how I split my data with a continuous target variable such that distrb. is preserved. One way to do it is bin your target variable: Use ...

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If you want a really simple place to start, you might do correlations between , say, shiner numbers and algae numbers. You might hope to see a nice line where shiner populations drop as algae populations rise. You could do Pearson correlations, where you use the numbers more or less as they are, (or you might log your numbers first, to make the outliers a ...

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That y_pred is determined by your regression equation, which is determined by your predictor (independent) variables, such as by $y_{pred}=X(X^TX)^{-1}X^Ty$ in an ordinary linear regression. This $y_{pred}$ usually is written as $\hat{y}$, as it is an estimate of $y$.

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Your ideas are similar to some already explored in the literature. The most important for success is that the modified samples are faithful to the underlying data distribution. If there is correlation between variables, randomizing individual variables may violate patterns in the data. One of the most commonly used methods is SMOTE: Synthetic Minority Over-...

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I have just seen following post with responses: https://stats.stackexchange.com/questions/502797/t-test-or-z-test-assume-normality-or-s≈σ Might give you an idea ?

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I'll start with referring to your comment: i am in preprocessing phase . and outlier should be removed in this phase It is not true that during data preprocessing you need to remove the outliers. By any means, this is not a default approach. Yes, you should remove invalid data, but by invalid we mean the data that is corrupted, or incorrect (e.g. human age ...

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