What is the impact of bias and variance on machine learning models?

What is the impact of bias and variance on machine learning models?

Bias and variance are two key sources of error in machine learning models. Bias refers to the error due to overly simplistic assumptions in the learning algorithm, while variance refers to the error due to excessive complexity in the model.

Can you explain how bias and variance affect model performance?

Certainly! High bias can lead to underfitting, where the model is too simple to capture the underlying patterns. High variance can lead to overfitting, where the model captures noise instead of the signal.

How can I balance bias and variance in my model?

You can balance bias and variance by using techniques like cross-validation, regularization, and selecting the right model complexity. It's often a trade-off.

What are some common methods to reduce bias or variance?

To reduce bias, you might try more complex models or adding features. To reduce variance, you can use techniques like bagging or dropout in neural networks.