Random Forests (RFs) can do classification or regression modeling.
Random Forests are one of the workhorse of modern machine learning. Especially, they cannot over-fit to the training set, are fast to train, predict fast, parallelize well and give you a reasonable model even without optimizing the model's default hyper-parameters. In other words, it is hard to shoot yourself in the foot while training or exploiting a Random Forests model. In comparison, with deep neural networks it is very easy to shoot yourself in the foot.
Using out of bag (OOB) samples, you can even get an idea of a RFs performance, without the need for a held out (test) data-set.
Their only drawback is that RFs, being an ensemble model, cannot predict values which are outside of the training set range of values (this is a serious limitation in case you are trying to optimize or minimize something in order to discover outliers, compared to your training set samples).
For the moment, this implementation only consider a sparse vector of integers as features. i.e. categorical variables will need to be one-hot-encoded. For classification, the dependent variable must be an integer (encoding a class label). For regression, the dependent variable must be a float.
Breiman, Leo. (1996). Bagging Predictors. Machine learning, 24(2), 123-140.
Breiman, Leo. (2001). Random Forests. Machine learning, 45(1), 5-32.
Geurts, P., Ernst, D., & Wehenkel, L. (2006). Extremely Randomized Trees. Machine learning, 63(1), 3-42.