As always, I strongly advice you to

As always, I strongly advice you to not use your favorite algorithm on every problem. You should at least be spot-checking a variety of different types of algorithms on a given problem.

For more on spot-checking algorithms, see my post “Why you should be Spot-Checking Algorithms on your Machine Learning Problems”.

That being said, decision trees often perform well on imbalanced datasets. The splitting rules that look at the class variable used in the creation of the trees, can force both classes to be addressed.

If in doubt, try a few popular decision tree algorithms like C4.5, C5.0, CART, and Random Forest.

Results (English) 2:

As always, I strongly advice you to not use your favorite algorithm on every problem. You should at Least be spot-checking a Variety of different types of algorithms on a GIVEN Problem.

For more on spot-checking algorithms, See My Post "Why You should be Spot-Checking Algorithms on your Machine Learning Problems".

That being said. , decision trees often perform well on imbalanced datasets. Rules that the splitting Look at the Class Variable used in the Creation of the Trees, Can Force both classes to be addressed.

If in doubt, a few popular TRY decision algorithms like Tree C4.5, C5.0, CART, and Random Forest. .

Results (English) 3:

As always I strongly, advice you to not use your favorite algorithm on every problem. You should at least be spot-checking. A variety of different types of algorithms on a given problem.For more on spot-checking algorithms see my, post "Why you should be Spot-Checking Algorithms on your Machine Learning. Problems ".That being said decision trees, often perform well on imbalanced datasets. The splitting rules that look at the class variable. Used in the creation of the trees can force, both classes to be addressed.If, in doubt try a few popular decision tree algorithms like C4.5 C5.0 CART,,, Random and Forest.