SVM doesn&#x27;t give CI either.  In fact, it doesn&#x27;t even give probability estimates per sample unless using Platt&#x27;s technique whose probabilities don&#x27;t always agree with the classifier&#x27;s outputs.<p>What I mean is a confidence interval on probabilities.  That is, if you did the training on 100 different training sets that follow the same distribution as your training set, they would have a range of outputs for a particular sample. The 95th highest and 5th lowest would have specific values.  These values can be estimated and are the bounds of the CI.<p>For example, for logistic regression, you can estimate standard error per coefficient like so: <a href="https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;Ordinary_least_squares#Finite_sample_properties" rel="nofollow">https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;Ordinary_least_squares#Finite_...</a>
That would give you a vector of standard errors the size of your variable(feature) set.  Then you can use that to estimate overall range of CI per sample.<p>This would be useful in the example provided in OP&#x27;s link. For example, let&#x27;s say that the classifier says there is 86% probability that the license plate is RBX735.<p>Can we really trust this 86% probability or is the model primarily relying on some information that is available in only 2 samples out of 1000 that might have been outliers?