A comic guide to underfitting

Underfitting in machine learning occurs when a model is unable to capture the underlying patterns of the data. This can happen for various reasons, such as using a model that is too simple or not having enough training data. When a model underfits the data, it performs poorly not only on the training set but also on new, unseen data. It is crucial to address underfitting by selecting more complex models, adding more features, or obtaining additional training data. Moreover, fine-tuning hyperparameters and optimizing the model’s architecture can also help mitigate underfitting issues and improve the model’s overall performance.

Underfitting can also happen when there are too few features in a model:

Underfitting can also happen when there are too few features in a model. In such cases, the model may struggle to capture the complexity of the underlying data, leading to poor performance and inaccurate predictions. Addressing this issue often involves identifying additional relevant features that can enrich the model’s representation of the data, ultimately improving its ability to make accurate predictions. Additionally, considering more advanced modeling techniques and fine-tuning hyperparameters can also help mitigate underfitting caused by a lack of features.