details_cubist_rules_Cubist {parsnip}

Cubist rule-based regression models

Description

Cubist::cubist() fits a model that derives simple feature rules from a tree ensemble and uses creates regression models within each rule. rules::cubist_fit() is a wrapper around this function.

Details

For this engine, there is a single mode: regression

Tuning Parameters

This model has 3 tuning parameters:

• committees: # Committees (type: integer, default: 1L)

• neighbors: # Nearest Neighbors (type: integer, default: 0L)

• max_rules: Max. Rules (type: integer, default: NA_integer)

Translation from parsnip to the underlying model call (regression)

The rules extension package is required to fit this model.

library(rules)

cubist_rules(
  committees = integer(1),
  neighbors = integer(1),
  max_rules = integer(1)
) %>%
  set_engine("Cubist") %>%
  set_mode("regression") %>%
  translate()

## Cubist Model Specification (regression)
## 
## Main Arguments:
##   committees = integer(1)
##   neighbors = integer(1)
##   max_rules = integer(1)
## 
## Computational engine: Cubist 
## 
## Model fit template:
## rules::cubist_fit(x = missing_arg(), y = missing_arg(), weights = missing_arg(), 
##     committees = integer(1), neighbors = integer(1), max_rules = integer(1))

Preprocessing requirements

This engine does not require any special encoding of the predictors. Categorical predictors can be partitioned into groups of factor levels (e.g. ⁠{a, c}⁠ vs ⁠{b, d}⁠) when splitting at a node. Dummy variables are not required for this model.

References

• 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.