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Majority class: Difference between revisions
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(Created page with "{{see also|Machine learning terms}} ==Introduction== In machine learning, the majority class is the more common label in a dataset that is imbalanced. For example, in a dataset where there are 80% "yes" and 20% "no", "yes" is the majority class. The opposite of the majority class the minority class. ==Impact on Model Performance== A majority class in a dataset can have an enormous effect on the performance of a machine le...") |
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==Impact on Model Performance== | ==Impact on Model Performance== | ||
A majority class in a dataset can have an enormous effect on the performance of a machine learning model. This occurs because the model may be biased towards predicting the majority class even when there is more | A majority class in a dataset can have an enormous effect on the performance of a [[machine learning model]]. This occurs because the model may be [[biased]] towards predicting the majority class even when there is more emphasis placed on a minority one for solving specific problems. This is known as a [[class imbalance]] and may lead to suboptimal performance if not addressed properly. | ||
Class imbalance can be addressed using various techniques, such as oversampling the minority class, undersampling the majority class or using both together. Another approach relies on weighting; where | Class imbalance can be addressed using various techniques, such as [[oversampling]] the minority class, [[undersampling]] the majority class or using both together. Another approach relies on [[weighting]]; where [[misclassify]]ing instances from one group at a higher cost than misclassifying from another. This can be accomplished either through cost-sensitive [[learning algorithm]]s or altering the [[loss function]] used during training. | ||
==Examples of Majority Class in Machine Learning== | ==Examples of Majority Class in Machine Learning== | ||