Suppose you want to train and evaluate a classifier on a very small training set of data. Which method would you choose to evaluate the classifier?  Why?

It's be that I don't fully get what you mean by "interpolation of traits."

Describe the main steps performed in a class-based paradigm to identify classes. The response is accurate. just using bullets Don't skip any steps.

Three classifiers are to be benchmarked. To this end, using the same data, the classifiers were trained and the following table shows the validation results obtained with n = 16 observations. 1 1 0 2 0 3 1 4 1 5 1 6 1 7 0 8 9 10 11 12 13 14 15 16 OTTOTOO 0 1 1 1 1 0 ZOOOoooo Hooo o Ytrue Y1 Y2 Y3 1 0 0 0 1 0 1 0 1 1 0 0 1 1 0 1 0 0 0 0 0 0 0 1 1 0 0 1 0 0 1 1 0 1 0 0 0 1 1 0 11O 1 1 1 1 0001 Match the classifiers with the performance measures. Accuracy and Error rate for Y3 Choose... Accuracy and Error rate for Y2 Choose... TPR and FPR for Y1 Choose...