  Databases compiled using ab-initio and symmetry-based calculations now
contain tens of thousands of topological insulators and topological semimetals.
This makes the application of modern machine learning methods to topological
materials possible. Using gradient boosted trees, we show how to construct a
machine learning model which can predict the topology of a given existent
material with an accuracy of 90%. Such predictions are orders of magnitude
faster than actual ab-initio calculations. Through extensive testing of
different models we determine which properties help detect topological
materials. We identify the sources of our model's errors and we discuss
approaches to overcome them.
