Banding settings¶
After pruning, each deployed interaction table is condensed into a small product grid of bands. Adjacent cells are merged where the model barely distinguishes them, cheapest merge first. Every table that contains a feature cuts it at the same nested places, so the bands line up across tables. Missing values always keep their own band.
How coarse the bands get is set by the model's own noise: the change to the predictions is held
within \((band\_tolerance \cdot \sigma)^2\), where \(\sigma\) is the spread between the bags. Banding
therefore needs bagging (n_bags of at least 2) and a pruned model (prune=True). The outcome
is recorded in pruning_report_["banding"]. See
Pruning, banding and graduation for details.
band_tolerance¶
Description¶
The tolerance of banding, as a multiple of the noise between the bags. The mean squared change
of the predictions caused by banding is held within \((band\_tolerance \cdot \sigma)^2\), and
within band_deviance_cap. Larger values give coarser bands.
None turns banding off.
Type
float
Default value
0.75
band_deviance_cap¶
Description¶
The maximum cost of banding, as a fraction of the model's training deviance. The noise tolerance alone could let a very noisy model move far; this cap bounds what that can cost.
Type
float
Default value
0.001 (0.1% of the training deviance)