  We present FOLD-SE, an efficient, explainable machine learning algorithm for
classification tasks given tabular data containing numerical and categorical
values. FOLD-SE generates a set of default rules-essentially a stratified
normal logic program-as an (explainable) trained model. Explainability provided
by FOLD-SE is scalable, meaning that regardless of the size of the dataset, the
number of learned rules and learned literals stay quite small while good
accuracy in classification is maintained. A model with smaller number of rules
and literals is easier to understand for human beings. FOLD-SE is competitive
with state-of-the-art machine learning algorithms such as XGBoost and
Multi-Layer Perceptrons (MLP) wrt accuracy of prediction. However, unlike
XGBoost and MLP, the FOLD-SE algorithm is explainable. The FOLD-SE algorithm
builds upon our earlier work on developing the explainable FOLD-R++ machine
learning algorithm for binary classification and inherits all of its positive
features. Thus, pre-processing of the dataset, using techniques such as one-hot
encoding, is not needed. Like FOLD-R++, FOLD-SE uses prefix sum to speed up
computations resulting in FOLD-SE being an order of magnitude faster than
XGBoost and MLP in execution speed. The FOLD-SE algorithm outperforms FOLD-R++
as well as other rule-learning algorithms such as RIPPER in efficiency,
performance and scalability, especially for large datasets. A major reason for
scalable explainability of FOLD-SE is the use of a literal selection heuristics
based on Gini Impurity, as opposed to Information Gain used in FOLD-R++. A
multi-category classification version of FOLD-SE is also presented.
