class_weight on sklearn's DecisionTreeClassifier
From sklearn's documentation, 


  The “balanced” mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as n_samples / (n_classes * np.bincount(y))


It puts bigger misclassification weights on minority classes than majority classes. This method has nothing to do with resampling; it modifies the misclassification cost matrix instead. 

Changing the misclassification cost of each class is a different approach from resampling approaches. In my opinion, it won't give exactly the same result as oversampling the minority class. Having said that, these two approaches are both helpful to dealing with imbalanced (or unbalanced) data classification