  Extreme events generated by complex systems have been intensively studied in
many fields due to their great impact on scientific research and our daily
lives. However, their prediction is still a challenge in spite of the
tremendous progress that model-free machine learning has brought to the field.
We experimentally generate, and theoretically model, extreme events in a
current-modulated, single-mode microcavity laser operating on orthogonal
polarizations, where their strongly differing thresholds -- due to cavity
birefringence -- give rise to giant light pulses initiated by spontaneous
emission. Applying reservoir-computing techniques, we identify in advance the
emergence of an extreme event from a time series, in spite of coarse sampling
and limited sample length. Performance is optimized through new hybrid
configurations that we introduce in this paper. Advance warning times can reach
5ns, i.e. approximately ten times the rise time of the individual extreme
event.
