The Dance of Disorder (Fluctuations of Entropy)

Q: What if SVR encounters Multi-collinearity  ?
1>We all know that if multi-colinearity exists, explanatory variables have a high degree of correlation between themselves which is problematic in all regression models.

2>Multicolinearity is not generally a problem for SVMs/SVRs.

3>Ridge Regression is a neat way to ensure you don't overfit your training data - essentially, you are desensitizing your model to the training data.
However, the variance of this new estimate can be so much lower than that of the least-squares estimator.

Ref:
https://stats.stackexchange.com/questions/35708/how-to-avoid-multicolinearity-in-svm-input-data
(How to avoid multicolinearity in SVM input data?)

https://tamino.wordpress.com/2011/02/12/ridge-regression/
(Ridge Regression)
https://statisticsbyjim.com/regression/multicollinearity-in-regression-analysis/
(Multicollinearity in Regression Analysis: Problems, Detection, and Solutions)
https://scikit-learn.org/stable/auto_examples/plot_kernel_ridge_regression.html