Hello…I’m diving deeper into data science and could use some clarity on convexity and optimization. Can someone explain what convexity means in the context of data science, and how it relates to optimization techniques? Thanks a lot for your insights…
In data science, convexity and optimization are important for finding the best model. For example, in linear regression, we minimize the mean squared error (MSE), a convex function. Because the MSE is convex, finding the minimum is easier and ensures a unique solution, improving model accuracy.
Does optimization/minimization problems means those kind of calculus problems when the gradient is zero, ie dy/dx=0 and u find the minimum point of a curve?