Underfitting occurs when a machine learning model is too simple to capture the underlying patterns in the data, resulting in poor performance. This can be problematic as the model fails to learn from the training data and therefore cannot make accurate predictions on new, unseen data.

One way to address underfitting is to select a more complex model that can better capture the complexity of the data. Linear regression, for example, may underfit a dataset with non-linear relationships, while a polynomial regression model would be better suited to capture the non-linear patterns. It is important, however, not to select a model that is too complex, as it may result in overfitting.

Increasing the number of features can also help combat underfitting. By considering more informative features, the model can have a higher chance of capturing complex relationships. However, it is crucial to avoid adding irrelevant or noisy features, as they may introduce unnecessary complexity and hinder the model's performance.

Furthermore, manipulating the training data can be beneficial in cases of underfitting. Techniques such as feature engineering, where additional features are derived from existing ones, can provide the model with more information to learn from. Additionally, resampling methods like oversampling or undersampling can be employed to balance imbalanced datasets and improve model performance.

Remember, finding the right balance between model complexity, feature selection, and data manipulation is essential when handling underfitting. By carefully selecting and tuning models, you can mitigate underfitting issues and improve your model's performance.