How to Develop a Reusable Framework to Spot-Check Algorithms in Python

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. How to Develop a Reusable Framework for Spot-Check Algorithms in Python Photo by Jeff Turner, some rights reserved. We cannot know beforehand what algorithms will perform well on a given predictive modeling problem.