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Overfitting cautious selection of classifier ensembles with genetic algorithms
Information Fusion, 2009Robert Sabourin
exaly
I tried a bunch of things: The dangers of unexpected overfitting in classification of brain data
Neuroscience and Biobehavioral Reviews, 2020Brad Wyble, Howard Bowman
exaly
Revista Electrónica AnestesiaR
Overfitting occurs when an algorithm over-learns the details of the training data, capturing not only the essence of the relationship between them, but also the random noise that will always be present. This negatively affects its performance and its ability to generalize when we introduce new data, not seen during training.
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Overfitting occurs when an algorithm over-learns the details of the training data, capturing not only the essence of the relationship between them, but also the random noise that will always be present. This negatively affects its performance and its ability to generalize when we introduce new data, not seen during training.
openaire +1 more source