Analytical vs Numerical Solutions in Machine Learning

Do you have questions like:

• What data is best for my problem?
• What algorithm is best for my data?
• How do I best configure my algorithm?

Why can’t a machine learning expert just give you a straight answer to your question?

In this post, I want to help you see why no one can ever tell you what algorithm to use or how to configure it for your specific dataset.

I want to help you see that finding good data/algorithm/configuration is in fact the hard part of applied machine learning and the only part you need to focus on solving.

Let’s get started.

Analytical vs Numerical Solutions

In mathematics, some problems can be solved analytically and numerically.

• An analytical solution involves framing the problem in a well-understood form and calculating the exact solution.
• A numerical solution means making guesses at the solution and testing whether the problem is solved well enough to stop.

An example is the
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