Learning from the machine: interpreting machine learning algorithms for
  point- and extended- source classification
  We investigate star-galaxy classification for astronomical surveys in the
context of four methods enabling the interpretation of black-box machine
learning systems. The first is outputting and exploring the decision boundaries
as given by decision tree based methods, which enables the visualization of the
classification categories. Secondly, we investigate how the Mutual Information
based Transductive Feature Selection (MINT) algorithm can be used to perform
feature pre-selection. If one would like to provide only a small number of
input features to a machine learning classification algorithm, feature
pre-selection provides a method to determine which of the many possible input
properties should be selected. Third is the use of the tree-interpreter package
to enable popular decision tree based ensemble methods to be opened,
visualized, and understood. This is done by additional analysis of the tree
based model, determining not only which features are important to the model,
but how important a feature is for a particular classification given its value.
Lastly, we use decision boundaries from the model to revise an already existing
method of classification, essentially asking the tree based method where
decision boundaries are best placed and defining a new classification method.
  We showcase these techniques by applying them to the problem of star-galaxy
separation using data from the Sloan Digital Sky Survey (hereafter SDSS). We
use the output of MINT and the ensemble methods to demonstrate how more complex
decision boundaries improve star-galaxy classification accuracy over the
standard SDSS frames approach (reducing misclassifications by up to
$\approx33\%$). We then show how tree-interpreter can be used to explore how
relevant each photometric feature is when making a classification on an object
by object basis.
