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 in machine learning that describes the balance between two types of errors: bias and variance.

Can you explain what bias and variance mean?

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 do I know if my model has high bias or high variance?

You can assess bias and variance by evaluating your model's performance on training and validation datasets. High bias typically results in poor performance on both, while high variance shows good training performance but poor validation performance.

What are some techniques to reduce bias or variance?

To reduce bias, you can use more complex models or add features. To reduce variance, you can simplify the model or use techniques like regularization.