Underfitting occurs when a machine learning model is too simple to capture the underlying patterns and relationships in the data. This can be caused by using overly simple models or insufficient training data. Here are some common causes and strategies to identify underfitting:
Overly simple models: Models that lack complexity or flexibility may struggle to capture the complexity of real-world data. For example, using a linear regression model for a non-linear relationship may lead to underfitting.
Insufficient training data: Inadequate amounts of training data can limit the model's ability to learn and generalize from patterns. Gathering more relevant data can help combat underfitting.
To identify underfitting, evaluation techniques such as model performance metrics (e.g., low accuracy, high error rate) and visualization of the data and predicted outcomes can be useful.
Remember, underfitting can lead to poor model performance and inaccurate predictions. It's essential to strike the right balance between model complexity and the amount of training data provided. Stay motivated and continue exploring strategies to mitigate underfitting!