What is the impact of bias and variance in machine learning?

What is the impact of bias and variance in machine learning?

Bias and variance are two 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 they 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?

You can balance bias and variance by using techniques like cross-validation, regularization, and selecting the right model complexity.

What are some common methods to reduce bias?

Common methods to reduce bias include using more complex models, increasing the amount of training data, and improving feature selection.