@data @datadon

Accuracy! To counter regression dilution, a method is to add a constraint on the statistical modeling.
Regression Redress restrains bias by segregating the residual values.
My article: http://data.yt/kit/regression-redress.html

@data @datadon

How to assess a statistical model?
How to choose between variables?

Pearson's #correlation is irrelevant if you suspect that the relationship is not a straight line.

If monotonic relationship:
"#Spearman’s rho is particularly useful for small samples where weak correlations are expected, as it can detect subtle monotonic trends." It is "widespread across disciplines where the measurement precision is not guaranteed".
"#Kendall’s Tau-b is less affected [than Spearman’s rho] by outliers in the data, making it a robust option for datasets with extreme values."
Ref: https://statisticseasily.com/kendall-tau-b-vs-spearman/

#AI #interpretability vs #explainability

"The explanations themselves can be difficult to convey to nonexperts, such as end users and line-of-business teams" https://www.techtarget.com/searchenterpriseai/feature/Interpretability-vs-explainability-in-AI-and-machine-learning

@data @datadon

Redressing #Bias: "Correlation Constraints for Regression Models":
Treder et al (2021) https://doi.org/10.3389/fpsyt.2021.615754

"In real life, we weigh the anticipated consequences of the decisions that we are about to make. That approach is much more rational than limiting the percentage of making the error of one kind in an artificial (null hypothesis) setting or using a measure of evidence for each model as the weight."
Longford (2005) http://www.stat.columbia.edu/~gelman/stuff_for_blog/longford.pdf

Feature Selection in Python; a script ready to use: https://johfischer.com/2021/08/06/correlation-based-feature-selection-in-python-from-scratch/

"Feature importance helps in understanding which features contribute most to the prediction"

A few lines with #sklearn: https://mljourney.com/sklearn-linear-regression-feature-importance/

@datadon

"The following sections discuss several state-of-the-art interpretable and explainable #ML methods. The selection of works does not comprise an exhaustive survey of the literature. Instead, it is meant to illustrate the commonest properties and inductive biases behind interpretable models and [black-box] explanation methods using concrete instances."
https://wires.onlinelibrary.wiley.com/doi/full/10.1002/widm.1493#widm1493-sec-0010-title