support support may be defined as the number of samples of the true response that lies in each class of target values score this score will give us the harmonic mean of precision and recall mathematicallyf score is the weighted average of the precision and recall the best value of would be and worst would be we can calculate score with the help of following formulaf (precision recall(precision recallf score is having equal relative contribution of precision and recall we can use classification_report function of sklearn metrics to get the classification report of our classification model auc (area under roc curveauc (area under curve)-roc (receiver operating characteristicis performance metricbased on varying threshold valuesfor classification problems as name suggestsroc is probability curve and auc measure the separability in simple wordsauc-roc metric will tell us about the capability of model in distinguishing the classes higher the aucbetter the model mathematicallyit can be created by plotting tpr (true positive ratei sensitivity or recall vs fpr (false positive ratei -specificityat various threshold values following is the graph showing rocauc having tpr at -axis and fpr at -axistpr roc aoc fpr we can use roc_auc_score function of sklearn metrics to compute auc-roc logloss (logarithmic lossit is also called logistic regression loss or cross-entropy loss it basically defined on probability estimates and measures the performance of classification model where the input is probability value between and it can be understood more clearly by differentiating it with accuracy as we know that accuracy is the count of predictions (predicted value actual valuein our model whereas log loss is the amount of uncertainty of our prediction based on how much it varies from the actual label with the