Why you should be Spot-Checking Algorithms on your Machine Learning Problems
Last Updated on August 16, 2020
Spot-checking algorithms is about getting a quick assessment of a bunch of different algorithms on your machine learning problem so that you know what algorithms to focus on and what to discard.
Photo by withassociates, some rights reserved
In this post you will discover the 3 benefits of spot-checking algorithms, 5 tips for spot-checking on your next problem and the top 10 most popular data mining algorithms that you could use in your suite of algorithms to spot-check.
Spot-Checking Algorithms
Spot-checking algorithms is a part of the process of applied machine learning. On a new problem, you need to quickly determine which type or class of algorithms is good at picking out the structure in your problem and which are not.
The alternative to spot checking is that you feel overwhelmed by the vast number of algorithms and algorithm types that you could try that you end up trying very few or going with what has worked for you in the past. This
To finish reading, please visit source site