Essential Evaluation Metrics for Classification Problems in Machine Learning

Accuracy is a measure of how well a model is performing overall. It is the proportion of correct predictions made by the model out of all the predictions made. In other words, it is the number of true positives and true negatives divided by the total number of predictions. Precision is a measure of how accurate the positive predictions of a model are. It is calculated as the ratio of true positives to the sum of true positives and false positives.