help of log loss valuewe can have more accurate view of the performance of our model we can use log_loss function of sklearn metrics to compute log loss example the following is simple recipe in python which will give us an insight about how we can use the above explained performance metrics on binary classification modelfrom sklearn metrics import confusion_matrix from sklearn metrics import accuracy_score from sklearn metrics import classification_report from sklearn metrics import roc_auc_score from sklearn metrics import log_loss x_actual [ y_predic [ results confusion_matrix(x_actualy_predicprint ('confusion matrix :'print(resultsprint ('accuracy score is',accuracy_score(x_actualy_predic)print ('classification report 'print (classification_report(x_actualy_predic)print('auc-roc:',roc_auc_score(x_actualy_predic)print('logloss value is',log_loss(x_actualy_predic)output confusion matrix [[ [ ]accuracy score is classification report precision recall -score support micro avg macro avg weighted avg auc-roc logloss value is