Evaluate Your Model

Evaluate Your Model

How do you know if your model is good?

How do you know if your model is good?

When I was a young ML gun, I believed that a model's accuracy was all that mattered. I soon realized how mistaken I was...

In the world of Machine Learning (ML) and Artificial Intelligence (AI), crafting a model is akin to setting sail on a vast ocean of data. To navigate these waters and reach our destination — a reliable and robust AI system — we need precise instruments. Evaluation metrics serve as these critical navigational tools, guiding you through the complex process of model selection, validation, and optimization. They help you answer essential questions: How well does your model perform? Where does it excel, and where does it falter? Without these metrics, you'd be adrift, unable to discern the effectiveness of your models or improve them over time.

In this tutorial, you'll learn how to:

  • Select Metrics That Match Your Project Goals: Dive into how choosing the right metrics can power your project towards its true objectives, making your AI solutions more impactful and aligned with what you're aiming to achieve.
  • Master Metrics for Classification Challenges: Uncover the secrets of precision, recall, and more, to effectively measure and boost the performance of your models in sorting and categorizing data accurately.
  • Navigate Through Regression Metrics: Explore the tools to precisely assess and refine how your models predict continuous outcomes, ensuring they hit the mark every time.
  • Combat Model Drift with Metrics Insight: Learn to identify and adapt to changes in your data environment, using metrics as your compass to maintain and enhance model accuracy over time.

In the spirit of learning by doing, we'll (almost) implement every metric from scratch and then compare our results with the built-in functions from popular libraries like scikit-learn. Let's set sail and chart a course through the seas of model evaluation!

Choosing the Right Metrics

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