predicted probabilities[ ]because the probabilities for the two classes sum to exactly one of the classes will be above certainty that class is the one that is predicted you can see in the previous output that the classifier is relatively certain for most points how well the uncertainty actually reflects uncertainty in the data depends on the model and the parameters model that is more overfitted tends to make more certain predictionseven if they might be wrong model with less complexity usually has more uncertainty in its predictions model is called calibrated if the reported uncertainty actually matches how correct it is--in calibrated modela prediction made with certainty would be correct of the time in the following example (figure - we again show the decision boundary on the datasetnext to the class probabilities for the class in[ ]figaxes plt subplots( figsize=( )mglearn tools plot_ d_separatorgbrtxax=axes[ ]alpha fill=truecm=mglearn cm scores_image mglearn tools plot_ d_scoresgbrtxax=axes[ ]alpha cm=mglearn reblfunction='predict_proba'for ax in axesplot training and test points mglearn discrete_scatter(x_test[: ]x_test[: ]y_testmarkers='^'ax=axmglearn discrete_scatter(x_train[: ]x_train[: ]y_trainmarkers=' 'ax=axax set_xlabel("feature "ax set_ylabel("feature "cbar plt colorbar(scores_imageax=axes tolist()axes[ legend(["test class ""test class ""train class ""train class "]ncol= loc= ) because the probabilities are floating-point numbersit is unlikely that they will both be exactly howeverif that happensthe prediction is made at random uncertainty estimates from classifiers