gamma gamma gamma accuracy accuracy accuracy auc auc auc figure - comparing roc curves of svms with different settings of gamma the accuracy of all three settings of gamma is the same this might be the same as chance performanceor it might not looking at the auc and the corresponding curvehoweverwe see clear distinction between the three models with gamma= the auc is actually at chance levelmeaning that the output of the decision_func tion is as good as random with gamma= performance drastically improves to an auc of finallywith gamma= we get perfect auc of that means that all positive points are ranked higher than all negative points according to the decision function in other wordswith the right thresholdthis model can classify the data perfectly! knowing thiswe can adjust the threshold on this model and obtain great predictions if we had only used accuracywe would never have discovered this for this reasonwe highly recommend using auc when evaluating models on imbalanced data keep in mind that auc does not make use of the default thresholdthoughso adjusting the decision threshold might be necessary to obtain useful classification results from model with high auc metrics for multiclass classification now that we have discussed evaluation of binary classification tasks in depthlet' move on to metrics to evaluate multiclass classification basicallyall metrics for multiclass classification are derived from binary classification metricsbut averaged looking at the curve for gamma= in detailyou can see small kink close to the top left that means that at least one point was not ranked correctly the auc of is consequence of rounding to the second decimal point model evaluation and improvement