Does running a Decision Tree classifier several times help?
The scikit-learn DecisionTreeClassifier takes a parameter called random_state. If this is set to an integer, your model should produce the same results every time.

The person suggesting you run the model many times would be correct, assuming you allow for no set random state. This means the results should be slightly different every time, because there is some random selection going on in the algorithm. Here is an example from the splitter classes:

# Draw a feature at random
f_j = rand_int(n_drawn_constants, f_i - n_found_constants,
               random_state)


If are are not setting that random state (or any other kind of random seed), I am not sure off the top of my head, why or how the results would always be identical.