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problems also arise when models grow too complex. This is a risk when considering real-world problems, where there’s always going to be uncertainty and errors in the data.

The temptation is to add variables to the algorithm until it explains everything in data perfectly, including the errors. This is known as overfitting, and it creates problems when you try to apply the same algorithm to different data.

When you realize that perfect algorithms don’t exist, you can relax your standards a bit and go for good enough instead of perfect.

Travelling Salesman Problem

Consider the travelling-salesman-problem, which asks: How can you find the single best route between multiple points without having to go anywhere twice. If you expand this problem to a whole state or country, it gets unspeakably complex.

In a situation like this, the most efficient solution is to relax your standards. Let your salesman visit different locations at least twice. You’ll end up close to a good solution in a reasonable time, even if it might not be a perfect one.

Understand the limits and possibilities algorithms can be helpful

Algorithms are not incomprehensible things that only exist to help mathematicians and computers. In fact, we use them every day, even if we’re unaware of it. Algorithms contain a lot of problem-solving wisdom that can help you make good decisions, predict probable outcomes and become a more productive individual.

Actionable advice:

Do the simple stuff first.