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.
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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