Underfitting occurs when a machine learning model is too simple to capture the underlying patterns in the data. This can lead to poor performance and an inability to effectively generalize to new instances. To mitigate underfitting, we can employ several techniques:
Increasing model complexity: If the initial model is too simple, it may not have enough capacity to capture the underlying complexity of the data. By using more complex algorithms or increasing the number of parameters, we can enhance the model's ability to fit the data.
Gathering more relevant data: Insufficient training data can contribute to underfitting. By collecting additional relevant data, we can provide more information for the model to learn from, enabling it to capture a broader range of patterns.
Feature engineering: Often, the provided features may not be adequate to capture the underlying patterns. Feature engineering involves transforming existing features or creating new ones that better represent the problem at hand, thereby improving the performance of the model.
Remember, finding the right balance between model complexity and the amount of available data is key to mitigating underfitting.