Hello IBM community,

There's no denying that libraries like Scikit-learn have democratized Machine Learning, allowing us to train models with just a few lines of code. However, this has created a risk: that of becoming "button-pushers" without understanding the mechanics behind the algorithms.

We know that Linear Algebra is the foundation, but I want to go beyond theory. Let's talk about practical and indispensable application.

My question is: when using a seemingly simple model like Linear Regression or Logistic Regression in Scikit-learn, how many of us stop to consider the critical matrix operation happening behind the scenes?

The normal equation (X.T * X)^-1 * X.T * y for Linear Regression, or the use of SVD Decomposition to solve this system in a numerically stable way, are examples of this. Ignoring this means treating the model as a black box.

To spark discussion, I'd like to pose a more specific question:

Beyond Linear Regression, in which other Scikit-learn algorithms are Eigendecomposition or Singular Value Decomposition (SVD) non-optional mathematical components for their basic functionality, and how does mastering this concept radically change how you interpret the model's results?

Let's consider PCA, LDA, or even matrix factorization methods. Let's share experiences: did you jump straight to .fit() or did your journey through Linear Algebra come first? How has this impacted your work?

Best regards to all, and let's evolve together.