What is the bias-variance tradeoff in deep learning?

What is the bias-variance tradeoff in deep learning?

The bias-variance tradeoff is a fundamental concept in machine learning that describes the balance 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 bias and variance affect model performance?

High bias can cause an algorithm to miss relevant relations between features and target outputs, leading to underfitting. High variance, on the other hand, can cause an algorithm to model the random noise in the training data, leading to overfitting.

Can you give an example of bias and variance in a model?

Sure! For instance, a linear regression model might have high bias if the true relationship is quadratic, leading to underfitting. Conversely, a very complex model like a deep neural network might fit the training data perfectly but perform poorly on unseen data due to high variance.

How can I reduce bias or variance in my model?

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