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==Strategies to reduce False Negatives== | ==Strategies to reduce False Negatives== | ||
There are several strategies to reduce false negatives in machine learning models. One of the most efficient solutions is using more [[training data]], which helps the model capture all distributions of data. Furthermore, data augmentation techniques like [[oversampling]] minority [[ | There are several strategies to reduce false negatives in machine learning models. One of the most efficient solutions is using more [[training data]], which helps the model capture all distributions of data. Furthermore, data augmentation techniques like [[oversampling]] minority [[class]]es or undersampling majority classes can help balance out the [[dataset]]. | ||
One strategy is to use a different evaluation metric such as [[precision]] or [[F1 score]], which accounts for both false positives and false negatives. Precision measures the percentage of true positive predictions out of all positive predictions, while F1 score is an average of precision and recall. | One strategy is to use a different evaluation metric such as [[precision]] or [[F1 score]], which accounts for both false positives and false negatives. Precision measures the percentage of true positive predictions out of all positive predictions, while F1 score is an average of precision and recall. | ||