Training set, validation set, and test set with Orange Is it possible with Orange (only using its widgets, without writing Python code) to implement the following typical machine learning processes? 


Train a training set,
Validating a validation set (e.g. using k-fold cross validation), and
Testing the model with a test set.


Using the 'Test &amp; Score" widget, a single dataset is used. Does this mean that only validation is performed, and there is no actual test? Can in this case classification results (accuracy, AUC, etc.) considered reliable?
The answer is yes. If Test &amp; Score is given only one data set, then all it can do is show results of cross-validation.

To test the models on a separate data set, use separate File widgets to load training and test data. Connect File widget with training data to Test &amp; Score, and the connect File widget with Test data to Test &amp; Score. The connect whatever learner to test (in the workflow on the figure I've used logistic regression and random forest). Make sure "Test on train data" is selected in the Test &amp; Score.