What causes underfitting in machine learning models?

What causes underfitting in machine learning models?

Underfitting occurs when a model is too simple to capture the underlying patterns in the data. This can happen due to insufficient features, overly simplistic algorithms, or inadequate training.

Can you give me examples of models that might underfit?

Sure! Linear regression on a non-linear dataset is a common example. Also, using a decision tree with very few splits can lead to underfitting.

How can I tell if my model is underfitting?

You can check the model's performance on both training and validation datasets. If both have high error rates, it's likely 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.

Is underfitting the same as overfitting?

No, underfitting occurs when a model is too simple, while overfitting happens when a model is too complex and captures noise in the data.