Identifying Overfitting and Underfitting Using Learning Curves

Understanding Learning Curve

Learning curves are graphical representations that illustrate how a model’s performance changes with increasing experience, typically measured by the amount of training data it has processed. The x-axis of a learning curve typically represents the amount of training data or the number of training iterations, while the y-axis represents the performance metric, such as error or accuracy....

Identifying Overfitting and Underfitting Using Learning Curves

Learning curves visually depict the model’s performance on both the training and validation sets over time. By analyzing these curves, we can identify overfitting and underfitting:...

Implementation of Learning Curve To Identify Overfitting and Underfitting

Here, we’ll demonstrate how learning curves can help identify overfitting and underfitting using the California Housing dataset, a popular dataset for regression tasks. These learning curves will visualize how the model’s performance evolves as it learns from a training set, compared to its performance on a validation set that it hasn’t seen during training. We will examine well-fitted, overfit and underfit models, focusing on training loss and validation loss to gain insights into their behaviors....

Conclusion

A large gap may indicate overfitting, whereas low scores for both may signal underfitting. By assessing a model’s learning curve, one can make informed decisions on whether to collect more data, try a more complex or simpler model, or adjust the existing one for better performance....