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highlighted code won't assign the value to the y column. It will raise key_error of y later.
However, with y being NaN, distr_prob = dat.groupby(['ae', 'bin'])\ ['y'].agg([good, bad])\ .assign(N=lambda x: x.good+x.bad, badprob=lambda x: x.bad/(x.good+x.bad) ).reset_index()
won't have distr attribute at the step of ploting.
Normally, PSI won't require label data and plot should still be okay to have without the line-plot of bad probability.
The text was updated successfully, but these errors were encountered:
highlighted code won't assign the value to the y column. It will raise key_error of y later.
However, with y being NaN,
distr_prob = dat.groupby(['ae', 'bin'])\ ['y'].agg([good, bad])\ .assign(N=lambda x: x.good+x.bad, badprob=lambda x: x.bad/(x.good+x.bad) ).reset_index()
won't have distr attribute at the step of ploting.
Normally, PSI won't require label data and plot should still be okay to have without the line-plot of bad probability.
The text was updated successfully, but these errors were encountered: