  We propose polar encoding, a representation of categorical and numerical
$[0,1]$-valued attributes with missing values to be used in a classification
context. We argue that this is a good baseline approach, because it can be used
with any classification algorithm, preserves missingness information, is very
simple to apply and offers good performance. In particular, unlike the existing
missing-indicator approach, it does not require imputation, ensures that
missing values are equidistant from non-missing values, and lets decision tree
algorithms choose how to split missing values, thereby providing a practical
realisation of the "missingness incorporated in attributes" (MIA) proposal.
Furthermore, we show that categorical and $[0,1]$-valued attributes can be
viewed as special cases of a single attribute type, corresponding to the
classical concept of barycentric coordinates, and that this offers a natural
interpretation of polar encoding as a fuzzified form of one-hot encoding. With
an experiment based on twenty real-life datasets with missing values, we show
that, in terms of the resulting classification performance, polar encoding
performs better than the state-of-the-art strategies "multiple imputation by
chained equations" (MICE) and "multiple imputation with denoising autoencoders"
(MIDAS) and -- depending on the classifier -- about as well or better than
mean/mode imputation with missing-indicators.
