Let a decision tree model $M$ be used to classify 1000 independent test examples. We consider the following events:

• $A$: $M$ classifies the third test example as positive;
• $B$: $M$ classifies the third test example as negative.

The model $M$ is fixed and deterministic. In both cases $A$ and $B$, we consider the same third test example.

Select all valid answers.

A polynomial kernel SVM is estimated on a validation set to have a 80% classification accuracy. When this model is run to classify a stream of new test examples, what is the expected number of examples to be processed until the first classification error appears (include the first error in the total number of examples required)?

How do you expect this number to vary according to the actual set of test examples? Report the standard deviation of your estimator.

When rounding, give at least 3 decimals.

What is the expected number of examples to be processed until the 6-th classification error is observed (include all 6 errors in the total number of examples required)?