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cross entropy loss에 대한 설명을 하실 때 언급한 gold data에 대해서 더 찾아보았습니다.
What gold data means?
This refers to data of very high quality, which is more or less as close as you can get to the ground truth. For example, Alzheimer's disease can be diagnosed through behavioral tests, but it's not a perfect diagnosis and can be confused with other types of dementia.
What is gold standard NLP?
In natural language processing (NLP) and computational linguistics the Gold Standard typically represents a corpus of text or a set of documents, annotated or tagged with the desired results for the analysis – be it designation of the corresponding part of speech, syntactic parsing, concept or relationship.
What is the gold standard validation strategy in machine learning?
Generally k-fold cross validation is the gold-standard for evaluating the performance of a machine learning algorithm on unseen data with k set to 3, 5, or 10. Using a train/test split is good for speed when using a slow algorithm and produces performance estimates with lower bias when using large datasets.
The text was updated successfully, but these errors were encountered:
cross entropy loss에 대한 설명을 하실 때 언급한 gold data에 대해서 더 찾아보았습니다.
What gold data means?
This refers to data of very high quality, which is more or less as close as you can get to the ground truth. For example, Alzheimer's disease can be diagnosed through behavioral tests, but it's not a perfect diagnosis and can be confused with other types of dementia.
What is gold standard NLP?
In natural language processing (NLP) and computational linguistics the Gold Standard typically represents a corpus of text or a set of documents, annotated or tagged with the desired results for the analysis – be it designation of the corresponding part of speech, syntactic parsing, concept or relationship.
What is the gold standard validation strategy in machine learning?
Generally k-fold cross validation is the gold-standard for evaluating the performance of a machine learning algorithm on unseen data with k set to 3, 5, or 10. Using a train/test split is good for speed when using a slow algorithm and produces performance estimates with lower bias when using large datasets.
The text was updated successfully, but these errors were encountered: