What are some solutions to underfitting in AI training?

What are some solutions to underfitting in AI training?

Underfitting occurs when a model is too simple to capture the underlying patterns in the data. Here are some solutions:

1. Increase model complexity by adding more layers or neurons.
2. Use more relevant features or perform feature engineering.
3. Reduce regularization to allow the model to fit the training data better.
4. Train the model for a longer period to improve learning.

Can you explain what regularization is?

Regularization is a technique used to prevent overfitting by adding a penalty to the loss function for large coefficients. It helps to keep the model simpler and more generalizable.

How can I tell if my model is underfitting?

You can tell if your model is underfitting by checking its performance on both the training and validation datasets. If both have high error rates, it indicates that the model is not learning well.

What are some common causes of underfitting?

Common causes of underfitting include using a model that is too simple, insufficient training time, and not having enough relevant features in the dataset.