There are a large number of Machine Learning (ML) algorithms. In this article, I am going to describe and outline pro and cons of common supervised ML algorithms.
Please read FinTechExplained disclaimer.
If you want to understand what supervised machine learning is then have a look at my article:
One Algorithm Can’t Solve All Machine Learning Problems
Choosing the optimal algorithm for your problem is dependent on a number of features of the algorithm. The features include speed, forecast accuracy, training time, amount of data required to train, how easy is it to implement and how difficult is to explain it to others. A large task of data scientist is to discuss and explain patterns and ML algorithms therefore it is important we understand the algorithm properly.
Machine Learning Algorithms can be grouped into three categories:
Supervised Algorithms Comparison
This family of algorithms can be used to find relationships between data
If you want to learn about machine learning in general then have a look at my article:
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.
This article aimed to outline common supervised machine learning algorithms.
Hope it helps.