I loved its frequent patterns tools apriori and association rules because other common libraries did not have it back then and when I could find those in Mlxtend which was easy to implement, I was so happy.
I also liked how easy it was create ensembled models with Mlxtend's VoteClassifier tools where I was able to test both soft and hard voting for my classification problems. Review collected by and hosted on G2.com.
Even though it does not take huge preprocessing effort before using apriori and association rules functions, it does require some. Besides the format was not explicitly given in the documentation so I spent time on this. Review collected by and hosted on G2.com.


