Simple 3-Step Methodology To The Best Machine Learning Algorithm
Last Updated on August 15, 2020
How do you choose the best algorithm for your dataset?
Machine learning is a problem of induction where general rules are learned from specific observed data from the domain.
It infeasible (impossible?) to know what representation or what algorithm to use to best learn from the data on a specific problem before hand, without knowing the problem so well that you probably don’t need machine learning to begin with.
So what algorithm should you use on a given problem? It’s a question of trial and error, or searching for the best representation, learning algorithm and algorithm parameters.
In this post, you will discover the simple 3-step methodology for finding the best algorithm for your problem proposed by some of the best predictive modelers in the business.
Steps To The Best Machine Learning Algorithm
Photo by David Goehring, some rights reserved.
3-Step Methodology
Max Kuhn is the creator and owner of the caret package for that provides a suite of tools for predictive modeling
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