Stop Overfitting in Machine Learning: 5 Proven Fixes That Work Fast

Avoid overfitting and underfitting in machine learning with simple fixes, regularization tips, and hyperparameter tuning that boost real-world model performance fast.

Machine learning is really just about spotting patterns in data and using those patterns to make smart guesses or decisions.

But if you’ve been following along in this series—from learning about the basics to supervised and unsupervised learning—you’ve probably picked up on something important: just building a model isn’t enough… Sorry nerds.

A model might do great on the data it was trained on, but then completely flop when it sees new data.

And that’s what I’m focusing on today: what happens when a model either learns too much or too little, and how we can fix that using techniques like regularization and tuning the model’s settings.

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In this article, I’m going to break down why models sometimes don’t hold up in the real world, how to keep that from happening, and how to fine-tune them so they actually work outside the lab.

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Okay, let’s first kick this off by looking a the pitfalls of Machine Learning. What are some common mistake and how can you prevent them?