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Class-imbalanced dataset: Difference between revisions
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{{see also|Machine learning terms}} | |||
==Introduction== | ==Introduction== | ||
'''Class imbalance''' is a frequent issue in [[machine learning]], where one or more [[class]]es in a [[dataset]] have significantly fewer [[example]]s than others. This imbalance makes it difficult for machine learning [[algorithm]]s to accurately predict the [[minority class]], leading to biased and inaccurate [[models]]. | '''Class imbalance''' is a frequent issue in [[machine learning]], where one or more [[class]]es in a [[dataset]] have significantly fewer [[example]]s than others. This imbalance makes it difficult for machine learning [[algorithm]]s to accurately predict the [[minority class]], leading to biased and inaccurate [[models]]. | ||
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To help the computer become more knowledgeable about all things, we can use different tricks. One such trick is to show it more pictures of cats and birds so that it can learn about them too. | To help the computer become more knowledgeable about all things, we can use different tricks. One such trick is to show it more pictures of cats and birds so that it can learn about them too. | ||
[[Category:Terms]] [[Category:Machine learning terms]] | |||