Please can these terms be clarified with their differences as they appear to all indicate the same thing in machine learning:

Overfitting vs high variance vs large estimation error vs high true risk vs low generalisation.

- and -

Underfitting vs high bias vs large approximation error vs large empirical risk vs large test error.

Can all these terms effectively be used interchangeably?

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Please can these terms be clarified with their differences as they appear to all indicate the same thing in machine learning:

Overfitting vs high variance vs large estimation error vs high true risk vs low generalisation.

- and -

Underfitting vs high bias vs large approximation error vs large empirical risk vs large test error.

Can all these terms effectively be used interchangeably?

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