Improving Machine Learning Outcomes

Published in Towards Data Science   By John Hawkins, Chief Scientist at Playground XYZ. Computer scientist, open source developer and the author of getting-data-science-done.com

Improving Machine Learning Outcomes

Focusing on Framing, Timing, and Targets — Introduction In order to build successful machine learning solutions, there are certain fundamental ideas that everyone involved needs to understand. In this blog post, we look at three key early stages of the design process that managers can focus on to ensure that the project is headed toward a successful outcome. …

Machine Learning 

Introduction

In order to build successful machine learning solutions, there are certain fundamental ideas that everyone involved needs to understand. In this blog post, we look at three key early stages of the design process that managers can focus on to ensure that the project is headed toward a successful outcome.

This post presumes the reader already understands distinctions in machine learning such as supervised and unsupervised models, training and testing stages, and the overall machine learning lifecycle. Returning to the earliest stage of defining the business problem, we focus our attention on three key objectives.

Three Key Objectives: Framing, Timing, & Target

Each of these objectives are introduced to some extent in data science training. However, quite often they do not receive the emphasis they deserve. In part this is because, on the surface, they seem obvious: frame the problem, decide when predictions will be made, and determine a target variable for the model to learn from.

These are three major levers a project stakeholder can utilize to influence the likelihood of project success. For this reason, it’s important to dive deeper on each one in order to surface some of the subtleties that can differentiate between getting a model that solves your business problem, or not.  ..... '