As you might guess, underfitting is the opposite of overfitting: it occurs when your model is too simple to learn the underlying structure of the data. For example, a linear model of life satisfaction is prone to underfit; reality is just more complex than the model, so its predictions are bound to be inaccurate, even on the training examples. The main options to fix this problem are: Selecting a more powerful model, with more parameters Feeding better features to the learning algorithm (feature engineering) Reducing the constraints on the model (e.g., reducing the regularization
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