Machine Learning Model Mistakes: The #1 Reason Your Predictions Are Wrong

Most machine learning models fail due to poor evaluation. Learn the best metrics—MAE, RMSE, Precision, Recall, and F1-Score—to improve your model’s accuracy today.

When you're building a machine learning model, getting it to make predictions is just the beginning. The real challenge or the fun stuff is figuring out how well those predictions match reality.

Our models might seem impressive at first glance, but if you don’t measure its performance, you won’t know whether it’s actually useful or just guessing in a way that happens to look right sometimes.

Think of it like training for a marathon, not that I’ve ever ran one... But still, running every day is great, but if you never check your speed or track your progress, how will you know if you're getting better?

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You might feel like you're improving, but without measuring your time or endurance, you could be running in circles. Machine learning works the same way—if we don’t evaluate our model properly, we have no idea if it’s truly making the right predictions or just producing results that look okay on the surface.

In this article, I’m going to break down the key ways to evaluate machine learning models. Whether you're working with regression models or classification models, you need to understand the right metrics to judge how well your model is doing.

By the end of this article, you’ll not only know what these evaluation metrics are, but also how and when to use them effectively to improve your machine learning projects.

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Alrighty, time to start evaluating the right way!