Machine Learning Algorithms Comparison

There are a large number of Machine Learning (ML) algorithms available. In this article, I am going to describe and outline pro and cons of common ML algorithms.

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Algorithm Trial-And-Error

Choosing the right machine learning algorithm is based on trial-and-error. Although one can use brute force approach and try all possible algorithms to find the right algorithm but it can save us time and cost if we understand the differences between algorithms.

One Algorithm Can’t Solve All Machine Learning Problems

Choosing the optimal algorithm for your problem is dependent on its features such as speed, forecast accuracy, training time, amount of data required to train, how easy is it to implement, how difficult is to explain it to others because a large task of data scientist is to discuss and explain patterns and ML algorithms and most importantly if the algorithm solves your problem .

Machine Learning Algorithms can be grouped into three categories:

Supervised Algorithms Comparison

This family of algorithms can be used to find relationships between data