A Constant Model predicts the same constant value for any input data. The constant value is computed by optimizing the given scorer. For example, for MSE/RMSE, the constant is the (weighted) mean of the target column. For MAE, it is the (weighted) median. For other scorers like MAPE or custom scorers, the constant is found with an optimization process. For classification problems, the constant probabilities are the observed priors.
A constant model is meant as a baseline reference model. If it ends up being used in the final pipeline, a warning will be issued because that would indicate a problem in the dataset or target column (e.g., when trying to predict a random outcome).
A Decision Tree is a single (binary) tree model that splits the training data population into sub-groups (leaf nodes) with similar outcomes. No row or column sampling is performed, and the tree depth and method of growth (depth-wise or loss-guided) is controlled by hyper-parameters.
Follow the Regularized Leader (FTRL) is a DataTable implementation  of the FTRL-Proximal online learning algorithm proposed in . This implementation uses a hashing trick and Hogwild approach  for parallelization. FTRL supports binomial and multinomial classification for categorical targets, as well as regression for continuous targets.
Generalized Linear Models (GLM) estimate regression models for outcomes following exponential distributions. GLMs are an extension of traditional linear models. They have gained popularity in statistical data analysis due to:
the flexibility of the model structure unifying the typical regression methods (such as linear regression and logistic regression for binary classification)
the recent availability of model-fitting software
the ability to scale well with large datasets
Isolation Forest is useful for identifying anomalies or outliers in data. Isolation Forest isolates observations by randomly selecting a feature and then randomly selecting a split value between the maximum and minimum values of that selected feature. This split depends on how long it takes to separate the points. Random partitioning produces noticeably shorter paths for anomalies. When a forest of random trees collectively produces shorter path lengths for particular samples, they are highly likely to be anomalies.
LightGBM is a gradient boosting framework developed by Microsoft that uses tree based learning algorithms. It was specifically designed for lower memory usage and faster training speed and higher efficiency. Similar to XGBoost, it is one of the best gradient boosting implementations available. It is also used for fitting Random Forest, DART (experimental), and Decision Tree models inside of Driverless AI.
Note: LightGBM with GPUs is not currently supported on Power.
The RuleFit  algorithm creates an optimal set of decision rules by first fitting a tree model, and then fitting a Lasso (L1-regularized) GLM model to create a linear model consisting of the most important tree leaves (rules).
Note: MOJOs are not currently available for RuleFit models.
TensorFlow is an open source software library for performing high performance numerical computation. Driverless AI includes a TensorFlow NLP recipe based on CNN Deeplearning models.
Note: MOJOs are not currently available for TensorFlow models.
XGBoost is a supervised learning algorithm that implements a process called boosting to yield accurate models. Boosting refers to the ensemble learning technique of building many models sequentially, with each new model attempting to correct for the deficiencies in the previous model. In tree boosting, each new model that is added to the ensemble is a decision tree. XGBoost provides parallel tree boosting (also known as GBDT, GBM) that solves many data science problems in a fast and accurate way. For many problems, XGBoost is one of the best gradient boosting machine (GBM) frameworks today. Driverless AI supports XGBoost GBM and XGBoost DART (experimental) models.
 DataTable for Python, https://github.com/h2oai/datatable
 J. Friedman, B. Popescu. “Predictive Learning via Rule Ensembles”. 2005. http://statweb.stanford.edu/~jhf/ftp/RuleFit.pdf
 Niu, Feng, et al. “Hogwild: A lock-free approach to parallelizing stochastic gradient descent.” Advances in neural information processing systems. 2011. https://people.eecs.berkeley.edu/~brecht/papers/hogwildTR.pdf
 McMahan, H. Brendan, et al. “Ad click prediction: a view from the trenches.” Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2013. https://research.google.com/pubs/archive/41159.pdf