Overfitting vs Underfitting

Hello! In this post, we will be exploring the concepts of Overfitting and Underfitting.

Overfitting:

Overfitting is a modelling error that occurs when the model fits the training data too well.

As you can see above, the overfitted model is fit almost perfectly to the training data. The overall cost of the model to the training data be near 0, however, the accuracy of this model would be poor when used on testing data.

Underfitting:

Underfitting is when the model fits to the training data too simply; when the model isn't complex enough to adequately understand the trend/pattern of the training data.

As you can see from the image above, the model is just a linear line and does not fit to the training data very well; the model does not accurately understand the trend of the data and is fit too simply.

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