Performance Matrix in Machine Learning

Here are some of the most common performance metrics for machine learning:

• Confusion matrix: A confusion matrix is a table that summarizes the performance of a machine learning model. It shows the number of true positives, false positives, true negatives, and false negatives.
• True positive (TP): The model correctly predicts that the input is positive.
• False positive (FP): The model incorrectly predicts that the input is positive.
• True negative (TN): The model correctly predicts that the input is negative.
• False negative (FN): The model incorrectly predicts that the input is negative.
• Accuracy: Accuracy is the fraction of predictions that the model gets correct. It is calculated by dividing the number of correct predictions by the total number of predictions.
• Recall: Recall is the fraction of positive instances that the model correctly predicts. It is calculated by dividing the number of true positives by the sum of the true positives and false negatives.
• Precision: Precision is the fraction of predicted positive instances that are actually positive. It is calculated by dividing the number of true positives by the sum of the true positives and false positives.