Common Evaluation Metrics Used for Classification Tasks — An Introduction
Classification is a supervised learning task in machine learning that involves predicting the class label of a given input. There are several metrics used to evaluate the performance of a classification model.
Accuracy:
Accuracy is defined as the ratio of the number of correctly classified samples to the total number of samples. It is a useful metric when there is no significant class imbalance. Otherwise the model becomes biased towards the majority class.
Precision:
Precision is defined as the ratio of true positives to the total number of positive predictions.
It measures the proportion of positive predictions that are correct.
Useful when the cost of a false positive is high. For example, in medical diagnosis, a false positive can result in unnecessary treatment, which can be harmful to the patient.
Recall:
Recall is defined as the ratio of true positives to the total number of actual positives.
It measures the proportion of actual positive samples that are correctly predicted.
Recall is useful when the cost of a false negative is high. For example, in spam email…
