Bias and variance of an estimator

Описание к видео Bias and variance of an estimator

We discuss the question of the quality of an estimator. Given different training datasets, how close is an estimator to the real value of a parameter (what is its bias) and how spread are those estimations (what is its variance). We show that the mean square error of an estimator is the square of its bias plus its variance.

This video is part of a full course on Foundations of Machine Learning that is freely available at    • Welcome to the Foundations of Machine...  .

Coding assignments: https://github.com/ionpetre/FoundML_c...

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