machine learning algorithms -machine performance metrics there are various metrics which we can use to evaluate the performance of ml algorithmsclassification as well as regression algorithms we must carefully choose the metrics for evaluating ml performance becausehow the performance of ml algorithms is measured and compared will be dependent entirely on the metric you choose how you weight the importance of various characteristics in the result will be influenced completely by the metric you choose performance metrics for classification problems we have discussed classification and its algorithms in the previous herewe are going to discuss various performance metrics that can be used to evaluate predictions for classification problems confusion matrix it is the easiest way to measure the performance of classification problem where the output can be of two or more type of classes confusion matrix is nothing but table with two dimensions viz "actualand "predictedand furthermoreboth the dimensions have "true positives (tp)""true negatives (tn)""false positives (fp)""false negatives (fn)as shown belowactual true positives (tpfalse positives (fp false negatives (fntrue negatives (tnpredicted explanation of the terms associated with confusion matrix are as followstrue positives (tp)it is the case when both actual class predicted class of data point is true negatives (tn)it is the case when both actual class predicted class of data point is false positives (fp)it is the case when actual class of data point is predicted class of data point is