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Accuracy: Difference between revisions
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Accuracy is an invaluable metric when the classes in a data set are balanced, meaning there are approximately equal numbers of samples for each. In such cases, accuracy serves as a great indication of the model's overall performance. | Accuracy is an invaluable metric when the classes in a data set are balanced, meaning there are approximately equal numbers of samples for each. In such cases, accuracy serves as a great indication of the model's overall performance. | ||
However, when classes are imbalanced (one class with significantly more samples than the other), accuracy may not be an accurate measure of model performance. A model may achieve high accuracy by correctly predicting the majority class even if it performs poorly on the minority one. When dealing with imbalanced [[datasets]], other metrics like [[precision]], [[recall]] and [[F1 score]] may provide more insightful evaluations of model effectiveness. | However, when classes are imbalanced (one class with significantly more samples than the other), accuracy may not be an accurate measure of model performance. A model may achieve high accuracy by correctly predicting the [[majority class]] even if it performs poorly on the minority one. When dealing with imbalanced [[datasets]], other metrics like [[precision]], [[recall]] and [[F1 score]] may provide more insightful evaluations of model effectiveness. | ||
==How is Accuracy Calculated?== | ==How is Accuracy Calculated?== | ||