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I am reading into statistics in combination with machine learning and I came across the expression "population minimizer". I found no good explanation on what a population minimizer exactly means, so what exactly is it?

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A loss function

$$l(y, f)$$ measures how good we can use $f$ to predict $y$.

The smaller the result of the loss function $l$ is, the better the function $f$ is in performing the prediction task.

Expectation values are used since in practice, $Y$ and $X$ are random.

$$f_{pop}(.) = \arg\min_{f(.)} \mathbb{E}[l(Y,f(X)]$$

The population minimizer is the function $f$ that best approximates $Y$ and thus minimizes the expectation value $\mathbb{E}$ of the loss function $l$.

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