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Monotonic constrained priors for boosted regressions

Source: R/prior_gdb.R
GDBPrior.Rd

Monotonic constrains for gradient descent boosting models do not work in the same way as other priors where a specific coefficient or magnitude of importance is specified. Rather monotonic constraints enforce a specific directionality of regression coefficients so that for instance a coefficient has to be positive or negative.

Important: Specifying a monotonic constrain for the engine_gdb does not guarantee that the variable is retained in the model as it can still be regularized out.

Usage

GDBPrior(variable, hyper = "increasing", ...)

# S4 method for class 'character'
GDBPrior(variable, hyper = "increasing", ...)

Arguments

variable

A character matched against existing predictors variables.

hyper

A character object describing the type of constrain. Available options are 'increasing', 'decreasing', 'convex', 'concave', 'positive', 'negative' or 'none'.

...

Variables passed on to prior object.

Note

Similar priors can also be defined for the engine_xgboost via XGBPrior().

References

  • Hofner, B., Müller, J., & Hothorn, T. (2011). Monotonicity‐constrained species distribution models. Ecology, 92(10), 1895-1901.

See also

Prior, XGBPrior

Other prior: BARTPrior(), BARTPriors(), BREGPrior(), BREGPriors(), GDBPriors(), GLMNETPrior(), GLMNETPriors(), INLAPrior(), INLAPriors(), STANPrior(), STANPriors(), XGBPrior(), XGBPriors(), add_priors(), get_priors(), priors(), rm_priors()