White Paper

Understand Machine Learning metrics

“Do you often wonder if machine learning can be trusted? If you can use it to further your business? Our first data science guide explains precisely the metrics!

When you understand them, you also understand if it is accurate, precise and unbiased! In this guide, 14 data science metrics are explained with simple graphs and clear examples for regression, binary and multi-class classification. Do not hesitate to contact us if you have any questions, our data science experts will be happy to help!

AI claims to improve our lives

It claims it loud and clear in figures: by next year, 80% of emerging technologies will have AI foundations, AI saves Netflix $1 billion each year with 75% of what users watch coming from AI recommendations.

To build AI-based systems that users can justifiably trust, one needs to understand how accurate machine learning technologies are. A central problem in this context is that ML predictions’ quality is challenging to measure. Yet evaluations, comparisons, and improvements of ML models require quantifiable measures. That’s when ML and AI metrics come into play.

We can use a variety of AI metrics to measure the actual performances of algorithms. We, at MyDataModels, would like to share our understanding of these metrics with you throughout this white paper. We hope it can help better understand these metrics and are happy to hear your feedback.

Why should we use multiple metrics?

Both for classification and regression algorithms, TADA MyDataModels’ AI-driven analytics platform, proposes various metrics defined by the Artificial Intelligence scientific community.

Each metric is a means to evaluate the model’s performance from a different aspect. This slide set presents and explains these various metrics and provides examples of their use.

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