A Complete Guide to Classification Metrics: Beyond Accuracy

Imagine you’ve built an AI model to sort fruit. After training, you test it on 100 pieces of fruit (80 apples and 20 oranges). The model correctly identifies 85 of them. Is it a good model? Your first instinct might be to say it’s “85% accurate,” and therefore pretty good. But what if I told …

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Demystifying the Confusion Matrix: A Simple Guide for Beginners

“The only confusing thing about a confusion matrix is its name. 🤔”— Inspired by my friend Raymond’s FB post When diving into the world of machine learning, one of the most crucial tasks is evaluating how well your model performs. For classification tasks (where the goal is to assign items into distinct categories), the confusion …

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