What is one of the drawbacks of Empirical Risk Minimization?

Quiz on statistical learning framework, empirical minimization framework and PAC learning. Machine learning is a formal learning model.   Statistical learning is a subfield of machine learning that uses statistical methods to identify patterns and relationships in data. Statistical learning is the basis for machine learning algorithms.  The Empirical Risk Minimization (ERM) principle is a learning paradigm which consists in selecting the model with minimal average error over the training set. This so-called training error can be seen as an estimate of the risk (due to the law of large... Show more

Quiz on statistical learning framework, empirical minimization framework and PAC learning.

Machine learning is a formal learning model.
 
Statistical learning is a subfield of machine learning that uses statistical methods to identify patterns and relationships in data. Statistical learning is the basis for machine learning algorithms. 

The Empirical Risk Minimization (ERM) principle is a learning paradigm which consists in selecting the model with minimal average error over the training set. This so-called training error can be seen as an estimate of the risk (due to the law of large numbers), hence the alternative name of empirical risk.

Probably Approximately Correct (PAC) learning is a mathematical framework for analyzing machine learning. It was proposed in 1984 by Leslie Valiant. 

In PAC learning, the learner receives samples and selects a generalization function from a class of possible functions. The goal is to find a hypothesis, or model, that approximates the target concept as accurately as possible.

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