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 tradeoff between two types of errors: bias and variance. 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. If your model performs well on training data but poorly on validation data, it likely has high variance (overfitting). Conversely, if it performs poorly on both, it may have high bias (underfitting).

What techniques can I use to reduce bias or variance?

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

Can you give an example of a model with high bias?

A common example of high bias is a linear regression model applied to a non-linear dataset. The model oversimplifies the relationship, leading to poor predictions.