Explain bias variance

Question

Accepted Answer

Bias variance tradeoff is a key concept in machine learning that deals with the balance between underfitting and overfitting.

Bias refers to the error that is introduced by approximating a real-life problem, while variance refers to the amount by which the estimate of the target function will change if different training data was used.
High bias means the model is too simple and underfits the data, while high variance means the model is too complex and overfits the data.
The goal is to find the sweet spot between bias and variance, where the model generalizes well to new data.
Regularization techniques like L1 and L2 can help reduce variance, while increasing model complexity can help reduce bias.
Cross-validation can be used to estimate the bias and variance of a model.

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