Supervised Machine Learning Algorithms Comparison

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.

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If you want to understand what supervised machine learning is then have a look at my article:

Supervised Machine Learning: Regression Vs Classification

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:

Machine Learning In 8 Minutes

Summary

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.


Supervised Machine Learning Algorithms Comparison was originally published in FinTechExplained on Medium, where people are continuing the conversation by highlighting and responding to this story.

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