What is the impact of underfitting in machine learning models?

What is the impact of underfitting in machine learning models?

Underfitting occurs when a model is too simple to capture the underlying patterns in the data. This can lead to poor performance on both training and test datasets.

Can you give me an example of underfitting?

Sure! Imagine trying to fit a linear model to a complex, non-linear dataset. The linear model won't capture the data's complexity, resulting in high error rates.

How can I identify if my model is underfitting?

You can identify underfitting by checking the model's performance metrics. If both training and validation errors are high, it's a sign of underfitting.

What are some ways to fix underfitting?

To fix underfitting, you can try using a more complex model, adding more features, or reducing regularization.