User friendly, applies to many modeling algorithms, great documentation, easy to learn

It has all the tools to structure the machine learning problem efficiently and effectively. It has all kind of algorithms - supervised: linear regression, logistic regression, decision trees, random forest, gbm etc , unsupervised: kmeans, dbscans, spectral clustering, optics etc, and dimensionality reduction algorithms . An exhaustive list of clustering algorithms is implemented. It is possible to automate end-to-end model building workflow such as model building, comparison, selection using cross-validation or other approaches, storing the object for scoring or returning the prediction on unseen datasets. Documentations is very well written - it not only explains the function definition but gives a good background of underlying mathematics used in algorithms.

Documentation has great explanation and is very easy to implement.