  Classification of young stellar objects (YSOs) into different evolutionary
stages helps us to understand the formation process of new stars and planetary
systems. Such classification has traditionally been based on spectral energy
distributions (SEDs). An alternative approach is provided by supervised machine
learning algorithms. We attempt to classify a sample of Orion YSOs into
different classes, where each source has already been classified using
multiwavelength SED analysis. We used 8 different learning algorithms to
classify the target YSOs, namely a decision tree, random forest, gradient
boosting machine (GBM), logistic regression, na\"ive Bayes classifier,
$k$-nearest neighbour classifier, support vector machine, and neural network.
The classifiers were trained and tested by using a 10-fold cross-validation
procedure. As the learning features, we employed ten continuum flux densities
spanning from the near-IR to submm. With a classification accuracy of 82% (with
respect to the SED-based classes), a GBM algorithm was found to exhibit the
best performance. The lowest accuracy of 47% was obtained with a na\"ive Bayes
classifier. Our analysis suggests that the inclusion of the 3.6 $\mu$m and 24
$\mu$m flux densities is useful to maximise the YSO classification accuracy.
Although machine learning has the potential to provide a rapid and fairly
reliable way to classify YSOs, an SED analysis is still needed to derive the
physical properties of the sources, and to create the labelled training data.
The classification accuracies can be improved with respect to the present
results by using larger data sets, more detailed missing value imputation, and
advanced ensemble methods. Overall, the application of machine learning is
expected to be very useful in the era of big astronomical data, for example to
quickly assemble interesting target source samples for follow-up studies.
