From the course: Mistakes to Avoid in Machine Learning

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Overfitting your models

Overfitting your models

From the course: Mistakes to Avoid in Machine Learning

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Overfitting your models

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- [Instructor] Arguably the most common technical mistake in machine learning is referred to as overfitting. Overfitting is when your model captures patterns in your training data too well. Meaning it doesn't generalize well to unseen data. Essentially your model has become highly attuned to the noise in your training set rather than the signal. So here's an example, let's say you wanted to predict which of your coworkers would go out to lunch versus bring one from home. Eventually, after memorizing the car of each of your coworkers, you learned who went to lunch by looking out into the parking lot. Thus achieving a model with great predictive accuracy on your training set. However, this model would be highly confused if you introduced a new coworker. And your prediction would be meaningless. So if you see a large drop-off in performance between your train and test groups, your model is likely overfit. So how do…

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