What is the bias-variance tradeoff in machine learning?

What is the bias-variance tradeoff in machine learning?

The bias-variance tradeoff is a fundamental concept that describes the balance between two types of errors in a model: bias and variance.

Can you explain bias and variance in more detail?

Sure! 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.

How can I visualize bias and variance?

You can visualize bias and variance using learning curves, which plot training and validation errors against the size of the training dataset.

What happens if I have high bias?

High bias can lead to underfitting, where the model is too simple to capture the underlying patterns in the data.