  The Vera C. Rubin Observatory will increase the number of observed supernovae
(SNe) by an order of magnitude; however, it is impossible to spectroscopically
confirm the class for all the SNe discovered. Thus, photometric classification
is crucial but its accuracy depends on the not-yet-finalized observing strategy
of Rubin Observatory's Legacy Survey of Space and Time (LSST). We
quantitatively analyze the impact of the LSST observing strategy on SNe
classification using simulated multi-band light curves from the Photometric
LSST Astronomical Time-Series Classification Challenge (PLAsTiCC). First, we
augment the simulated training set to be representative of the photometric
redshift distribution per supernovae class, the cadence of observations, and
the flux uncertainty distribution of the test set. Then we build a classifier
using the photometric transient classification library snmachine, based on
wavelet features obtained from Gaussian process fits, yielding similar
performance to the winning PLAsTiCC entry. We study the classification
performance for SNe with different properties within a single simulated
observing strategy. We find that season length is important, with light curves
of 150 days yielding the highest performance. Cadence also has an important
impact on SNe classification; events with median inter-night gap <3.5 days
yield higher classification performance. Interestingly, we find that large gaps
(>10 days) in light curve observations do not impact performance if sufficient
observations are available on either side, due to the effectiveness of the
Gaussian process interpolation. This analysis is the first exploration of the
impact of observing strategy on photometric supernova classification with LSST.
