Which of the following statements accurately describe Empirical Risk Minimization (ERM)? Select all that apply.

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Seekh · Accepted Answer

Empirical Risk Minimization is the idea that a learning algorithm should pick the hypothesis that gives the lowest average loss on the training examples, called the empirical risk. The empirical risk is simply the sum (or mean) of the loss values for each training point, so minimizing it means the model fits the data as closely as possible. This principle can lead to overfitting if the hypothesis space is too large, because the model can fit noise in the training set. For example, in linear regression with squared loss, ERM chooses the line that makes the sum of squared differences between the predicted and actual values as small as possible on the training data. Thus, ERM is a concrete, data‑driven way to select a model by minimizing its average error on the observed samples.

Which of the following statements accurately describe Empirical Risk Minimization (ERM)? Select all that apply.

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