What is the difference between bias and variance in machine learning?

What is the difference between bias and variance in machine learning?

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 give me an example of high bias and high variance?

Sure! A high bias model might be a linear regression on a non-linear dataset, while a high variance model could be a decision tree that perfectly fits the training data.

How do I reduce bias and variance in my model?

To reduce bias, you can use more complex models or add features. To reduce variance, you can use techniques like regularization or cross-validation.

What is overfitting and underfitting?

Overfitting occurs when a model learns the training data too well, capturing noise as well as the signal. Underfitting happens when a model is too simple to capture the underlying trend.