cubist_rules: Cubist rule-based regression models

In parsnip: A Common API to Modeling and Analysis Functions

Cubist rule-based regression models

Description

cubist_rules() defines a model that derives simple feature rules from a tree ensemble and creates regression models within each rule. This function can fit regression models.

\Sexpr[stage=render,results=rd]{parsnip:::make_engine_list("cubist_rules")}

More information on how parsnip is used for modeling is at https://www.tidymodels.org/.

Usage

cubist_rules(
  mode = "regression",
  committees = NULL,
  neighbors = NULL,
  max_rules = NULL,
  engine = "Cubist"
)

Arguments

mode	A single character string for the type of model. The only possible value for this model is "regression".
committees	A non-negative integer (no greater than 100) for the number of members of the ensemble.
neighbors	An integer between zero and nine for the number of training set instances that are used to adjust the model-based prediction.
max_rules	The largest number of rules.
engine	A single character string specifying what computational engine to use for fitting.

Details

Cubist is a rule-based ensemble regression model. A basic model tree (Quinlan, 1992) is created that has a separate linear regression model corresponding for each terminal node. The paths along the model tree are flattened into rules and these rules are simplified and pruned. The parameter min_n is the primary method for controlling the size of each tree while max_rules controls the number of rules.

Cubist ensembles are created using committees, which are similar to boosting. After the first model in the committee is created, the second model uses a modified version of the outcome data based on whether the previous model under- or over-predicted the outcome. For iteration m, the new outcome ⁠y*⁠ is computed using

If a sample is under-predicted on the previous iteration, the outcome is adjusted so that the next time it is more likely to be over-predicted to compensate. This adjustment continues for each ensemble iteration. See Kuhn and Johnson (2013) for details.

After the model is created, there is also an option for a post-hoc adjustment that uses the training set (Quinlan, 1993). When a new sample is predicted by the model, it can be modified by its nearest neighbors in the original training set. For K neighbors, the model-based predicted value is adjusted by the neighbor using:

where t is the training set prediction and w is a weight that is inverse to the distance to the neighbor.

This function only defines what type of model is being fit. Once an engine is specified, the method to fit the model is also defined. See set_engine() for more on setting the engine, including how to set engine arguments.

The model is not trained or fit until the fit() function is used with the data.

Each of the arguments in this function other than mode and engine are captured as quosures. To pass values programmatically, use the injection operator like so:

value <- 1
cubist_rules(argument = !!value)

References

https://www.tidymodels.org, Tidy Modeling with R, searchable table of parsnip models

Quinlan R (1992). "Learning with Continuous Classes." Proceedings of the 5th Australian Joint Conference On Artificial Intelligence, pp. 343-348.

Quinlan R (1993)."Combining Instance-Based and Model-Based Learning." Proceedings of the Tenth International Conference on Machine Learning, pp. 236-243.

Kuhn M and Johnson K (2013). Applied Predictive Modeling. Springer.

See Also

Cubist::cubist(), Cubist::cubistControl(), \Sexpr[stage=render,results=rd]{parsnip:::make_seealso_list("cubist_rules")}

parsnip documentation built on June 24, 2024, 5:14 p.m.