classification evaluation metrics the job is not done even if you have finished implementation of your machine learning application or model we must have to find out how effective our model isthere can be different evaluation metricsbut we must choose it carefully because the choice of metrics influences how the performance of machine learning algorithm is measured and compared the following are some of the important classification evaluation metrics among which you can choose based upon your dataset and kind of problemconfusion 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 the 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 false negatives (fn)it is the case when actual class of data point is predicted class of data point is we can find the confusion matrix with the help of confusion_matrix(function of sklearn with the help of the following scriptwe can find the confusion matrix of above built binary classifier