Machine Learning

How to Develop a Framework to Spot-Check Machine Learning Algorithms in Python

Last Updated on August 28, 2020

Spot-checking algorithms is a technique in applied machine learning designed to quickly and objectively provide a first set of results on a new predictive modeling problem.

Unlike grid searching and other types of algorithm tuning that seek the optimal algorithm or optimal configuration for an algorithm, spot-checking is intended to evaluate a diverse set of algorithms rapidly and provide a rough first-cut result. This first cut result may be used to get an idea if a problem or problem representation is indeed predictable, and if so, the types of algorithms that may be worth investigating further for the problem.

Spot-checking is an approach to help overcome the “hard problem” of applied machine learning and encourage you to clearly think about the higher-order search problem being performed in any machine learning project.

In this tutorial, you will discover the usefulness of spot-checking algorithms on a new predictive modeling problem and how to develop a standard framework for spot-checking algorithms in python for classification and regression problems.

After completing this tutorial, you will know: