this may be a dumb one... The model mbg-cnbd-k.R has the ConditionalExpectedTransactions function, where there is code to correct for bias. My question is: why length >= 100? is this some rule of thumb? what's the rational behind it? This makes the forecasts conditional to the length of the vector, and not only to their past behaviour. Am I missing something?
I also do not understand the comment: "Only do so, if we can safely assume that the full customer cohort is passed."
# Adjust bias BG/NBD-based approximation by scaling via the Unconditional
# Expectations (for wich we have exact expression). Only do so, if we can
# safely assume that the full customer cohort is passed.
do.bias.corr <- k > 1 && length(x) == length(t.x)
&& length(x) == length(T.cal) && length(x) >= 100
this may be a dumb one... The model mbg-cnbd-k.R has the ConditionalExpectedTransactions function, where there is code to correct for bias. My question is: why length >= 100? is this some rule of thumb? what's the rational behind it? This makes the forecasts conditional to the length of the vector, and not only to their past behaviour. Am I missing something?
I also do not understand the comment: "Only do so, if we can safely assume that the full customer cohort is passed."