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5

It really depends on what words you put after "ground truth". Sometimes people will talk about "ground truth labels", for example in the context of classification or regression problems. The "ground truth labels" in such a case would refer to the true labels of instances; the labels that we use as target labels for instances ...


7

In the context of ML, ground truth refers to information provided by direct observation (empirical evidence). If you're training an algorithm to classify your data, then the ground truth will be the actual, true labels which could for example be manually annotated by an domain expert. Please note that the models prediction or the inferred labels, are not ...


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