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Every reasonable combination of Estimators should work.
import itertools import chainladder as cl tri = cl.load_sample('clrd').groupby('LOB').sum()[['CumPaidLoss', 'IncurLoss', 'EarnedPremDIR']] tri['CaseIncurredLoss'] = tri['IncurLoss'] - tri['CumPaidLoss'] X = tri[['CumPaidLoss', 'CaseIncurredLoss']] sample_weight = tri['EarnedPremDIR'] dev = [cl.Development(), cl.ClarkLDF(), cl.Trend(), cl.IncrementalAdditive(), cl.MunichAdjustment(paid_to_incurred=('CumPaidLoss', 'CaseIncurredLoss')), cl.CaseOutstanding(paid_to_incurred=('CumPaidLoss', 'CaseIncurredLoss'))] tail = [cl.TailCurve(), cl.TailConstant(), cl.TailBondy(), cl.TailClark()] ibnr = [cl.Chainladder(), cl.BornhuetterFerguson(), cl.Benktander(n_iters=2), cl.CapeCod()] for model in list(itertools.product(dev, tail, ibnr)): print(model) cl.Pipeline( steps=[('dev', model[0]), ('tail', model[1]), ('ibnr', model[2])] ).fit_predict(X, sample_weight=sample_weight).ibnr_.sum('origin').sum('columns').sum()
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Every reasonable combination of Estimators should work.
The text was updated successfully, but these errors were encountered: