# Cubist Regresion Models

Cubist is an R port of the Cubist GPL C code released by RuleQuest at http://rulequest.com/cubist-info.html. See the last section of this document for information on the porting. The other parts describes the functionality of the R package.

## Model Trees

Cubist is a rule-based model that is an extension of Quinlan’s M5 model tree. A tree is grown where the terminal leaves contain linear regression models. These models are based on the predictors used in previous splits. Also, there are intermediate linear models at each step of the tree. A prediction is made using the linear regression model at the terminal node of the tree, but is “smoothed” by taking into account the prediction from the linear model in the previous node of the tree (which also occurs recursively up the tree). The tree is reduced to a set of rules, which initially are paths from the top of the tree to the bottom. Rules are eliminated via pruning and/or combined for simplification.

This is explained better in Quinlan (1992). Wang and Witten (1997) attempted to recreate this model using a “rational reconstruction” of Quinlan (1992) that is the basis for the M5P model in Weka (and the R package RWeka).

An example of a model tree can be illustrated using the Ames housing data in the modeldata package.

library(Cubist)

data(ames, package = "modeldata")
# model the data on the log10 scale
ames$Sale_Price <- log10(ames$Sale_Price)

set.seed(11)
in_train_set <- sample(1:nrow(ames), floor(.8*nrow(ames)))

predictors <-
c("Lot_Area", "Alley", "Lot_Shape", "Neighborhood", "Bldg_Type",
"Year_Built", "Total_Bsmt_SF", "Central_Air", "Gr_Liv_Area",
"Bsmt_Full_Bath", "Bsmt_Half_Bath", "Full_Bath", "Half_Bath",
"TotRms_AbvGrd",  "Year_Sold", "Longitude", "Latitude")

train_pred <- ames[ in_train_set, predictors]
test_pred  <- ames[-in_train_set, predictors]

train_resp <- ames$Sale_Price[ in_train_set] test_resp <- ames$Sale_Price[-in_train_set]

model_tree <- cubist(x = train_pred, y = train_resp)
model_tree
##
## Call:
## cubist.default(x = train_pred, y = train_resp)
##
## Number of samples: 2344
## Number of predictors: 17
##
## Number of committees: 1
## Number of rules: 14
summary(model_tree)
##
## Call:
## cubist.default(x = train_pred, y = train_resp)
##
##
## Cubist [Release 2.07 GPL Edition]  Mon Jul  1 21:23:48 2024
## ---------------------------------
##
##     Target attribute outcome'
##
## Read 2344 cases (18 attributes) from undefined.data
##
## Model:
##
##   Rule 1: [48 cases, mean 4.859286, range 4.106837 to 5.176091, est err 0.114526]
##
##     if
##  Neighborhood in {Old_Town, Sawyer_West, Iowa_DOT_and_Rail_Road}
##  Year_Built <= 1952
##  Central_Air = N
##  Gr_Liv_Area <= 1692
##     then
##  outcome = -352.195658 + 10.59 Latitude + 0.000362 Gr_Liv_Area
##            - 0.044 Year_Sold
##
##   Rule 2: [59 cases, mean 4.981545, range 4.594393 to 5.311754, est err 0.066423]
##
##     if
##  Neighborhood in {North_Ames, Edwards, Sawyer, Brookside, Crawford,
##                          South_and_West_of_Iowa_State_University}
##  Year_Built <= 1952
##  Central_Air = N
##  Gr_Liv_Area <= 1692
##     then
##  outcome = 3.738655 + 0.000281 Gr_Liv_Area + 8e-05 Total_Bsmt_SF
##            + 0.00047 Year_Built
##
##   Rule 3: [333 cases, mean 5.089450, range 4.69897 to 5.676693, est err 0.063499]
##
##     if
##  Neighborhood in {Old_Town, Edwards, Gilbert, Sawyer, Sawyer_West,
##                          South_and_West_of_Iowa_State_University}
##  Year_Built <= 1952
##  Central_Air = Y
##     then
##  outcome = -36.367569 + 0.000242 Gr_Liv_Area + 5.9e-05 Total_Bsmt_SF
##            + 0.00085 Year_Built + 0.94 Latitude - 0.01 TotRms_AbvGrd
##            + 0.036 Bsmt_Half_Bath + 0.006 Bsmt_Full_Bath + 5e-07 Lot_Area
##
##   Rule 4: [176 cases, mean 5.137743, range 4.788875 to 5.332842, est err 0.034310]
##
##     if
##  Neighborhood in {College_Creek, Edwards, Somerset, Northridge_Heights,
##                          Blueste, Landmark}
##  Year_Built > 1952
##  Total_Bsmt_SF <= 765
##  Gr_Liv_Area <= 1692
##     then
##  outcome = -28.661356 + 0.00333 Year_Built + 0.000188 Gr_Liv_Area
##            + 7.4e-05 Total_Bsmt_SF + 5.6e-06 Lot_Area + 0.64 Latitude
##            - 0.007 TotRms_AbvGrd
##
##   Rule 5: [35 cases, mean 5.150411, range 4.954243 to 5.361728, est err 0.050057]
##
##     if
##  Neighborhood in {North_Ames, Old_Town, Sawyer, Northwest_Ames, Crawford,
##                          Green_Hills}
##  Year_Built > 1952
##  Total_Bsmt_SF <= 765
##  Gr_Liv_Area <= 1692
##     then
##  outcome = 87.372832 + 0.000171 Total_Bsmt_SF + 8.5e-05 Gr_Liv_Area
##            + 0.00127 Year_Built - 2.02 Latitude + 3.8e-06 Lot_Area
##            - 0.005 TotRms_AbvGrd
##
##   Rule 6: [76 cases, mean 5.157218, range 4.973128 to 5.363612, est err 0.045520]
##
##     if
##  Neighborhood in {North_Ames, Crawford}
##  Year_Built <= 1952
##  Central_Air = Y
##  Gr_Liv_Area <= 1692
##     then
##  outcome = 141.774591 + 0.000129 Gr_Liv_Area - 3.26 Latitude
##            + 4.2e-06 Lot_Area + 0.037 Bsmt_Full_Bath + 9e-05 Year_Built
##            + 6e-06 Total_Bsmt_SF
##
##   Rule 7: [54 cases, mean 5.171031, range 4.79588 to 5.44832, est err 0.051527]
##
##     if
##  Bldg_Type in {Duplex, Twnhs}
##  Gr_Liv_Area > 1692
##     then
##  outcome = 2.31639 + 0.000232 Gr_Liv_Area + 0.00125 Year_Built
##            - 0.018 TotRms_AbvGrd + 6.1e-05 Total_Bsmt_SF + 9e-07 Lot_Area
##            + 0.01 Half_Bath + 0.007 Bsmt_Full_Bath
##
##   Rule 8: [473 cases, mean 5.192192, range 4.826075 to 5.509555, est err 0.037515]
##
##     if
##  Neighborhood in {North_Ames, College_Creek, Old_Town, Edwards, Gilbert,
##                          Sawyer, Northwest_Ames, Sawyer_West, Mitchell,
##  Total_Bsmt_SF > 765
##  Gr_Liv_Area <= 1692
##  Bsmt_Full_Bath > 0
##     then
##  outcome = 23.436857 + 0.00245 Year_Built + 0.000128 Gr_Liv_Area
##            + 0.000103 Total_Bsmt_SF + 2.5e-06 Lot_Area + 0.015 Full_Bath
##            + 0.25 Longitude + 0.003 TotRms_AbvGrd
##
##   Rule 9: [480 cases, mean 5.198712, range 4.778151 to 5.463805, est err 0.040757]
##
##     if
##  Year_Built > 1952
##  Total_Bsmt_SF > 765
##  Gr_Liv_Area <= 1692
##  Bsmt_Full_Bath <= 0
##     then
##  outcome = 18.268736 + 0.000174 Gr_Liv_Area + 0.00279 Year_Built
##            + 8.9e-05 Total_Bsmt_SF + 3.3e-06 Lot_Area
##            - 0.011 TotRms_AbvGrd + 0.016 Bsmt_Full_Bath + 0.26 Longitude
##            + 0.13 Latitude
##
##   Rule 10: [315 cases, mean 5.297982, range 4.905256 to 5.676693, est err 0.051186]
##
##     if
##  Neighborhood in {North_Ames, College_Creek, Old_Town, Edwards, Gilbert,
##                          Sawyer, Northwest_Ames, Sawyer_West, Mitchell,
##                          South_and_West_of_Iowa_State_University,
##  Bldg_Type in {OneFam, TwoFmCon, TwnhsE}
##  Year_Built <= 2004
##  Gr_Liv_Area > 1692
##     then
##  outcome = -28.023112 + 0.000157 Gr_Liv_Area + 0.0015 Year_Built
##            + 8.4e-05 Total_Bsmt_SF - 0.015 TotRms_AbvGrd + 0.03 Full_Bath
##            + 0.029 Half_Bath + 2.3e-06 Lot_Area - 0.32 Longitude
##            + 0.015 Bsmt_Full_Bath
##
##   Rule 11: [161 cases, mean 5.445366, range 5.142662 to 5.872156, est err 0.052062]
##
##     if
##  Neighborhood in {Somerset, Northridge_Heights, Brookside, Crawford,
##                          Northridge, Stone_Brook, Clear_Creek}
##  Bldg_Type in {OneFam, TwoFmCon, TwnhsE}
##  Year_Built <= 2004
##  Gr_Liv_Area > 1692
##     then
##  outcome = 2.344921 + 0.000151 Gr_Liv_Area + 0.00134 Year_Built
##            + 8.3e-05 Total_Bsmt_SF + 0.034 Bsmt_Full_Bath
##            - 0.004 TotRms_AbvGrd + 0.007 Half_Bath + 6e-07 Lot_Area
##
##   Rule 12: [275 cases, mean 5.452962, range 5.136721 to 5.872156, est err 0.051166]
##
##     if
##  Neighborhood in {Somerset, Northridge_Heights, Crawford, Northridge,
##                          Stone_Brook, Clear_Creek, Veenker, Blueste, Greens,
##                          Green_Hills}
##  Year_Built > 1952
##  Bsmt_Full_Bath > 0
##     then
##  outcome = 19.156714 + 0.000178 Gr_Liv_Area + 0.000159 Total_Bsmt_SF
##            + 0.00176 Year_Built + 1.4e-06 Lot_Area + 0.19 Longitude
##            + 0.007 Bsmt_Full_Bath - 0.002 TotRms_AbvGrd
##
##   Rule 13: [113 cases, mean 5.491452, range 5.281034 to 5.765619, est err 0.039038]
##
##     if
##  Year_Built > 2004
##  Total_Bsmt_SF <= 1907
##  Gr_Liv_Area > 1692
##     then
##  outcome = 25.674216 + 0.0097 Year_Built + 0.000152 Gr_Liv_Area
##            + 0.000109 Total_Bsmt_SF + 0.057 Bsmt_Full_Bath
##            + 0.68 Longitude + 0.56 Latitude
##
##   Rule 14: [35 cases, mean 5.602593, range 5.20412 to 5.786508, est err 0.077426]
##
##     if
##  Year_Built > 2004
##  Total_Bsmt_SF > 1907
##     then
##  outcome = -0.069641 - 9.9e-05 Gr_Liv_Area + 0.008 Bsmt_Full_Bath
##            + 0.14 Latitude + 0.001 TotRms_AbvGrd
##
##
## Evaluation on training data (2344 cases):
##
##     Average  |error|           0.053913
##     Relative |error|               0.39
##     Correlation coefficient        0.90
##
##
##  Attribute usage:
##    Conds  Model
##
##     80%    97%    Year_Built
##     76%   100%    Gr_Liv_Area
##     74%           Neighborhood
##     50%    97%    Total_Bsmt_SF
##     47%    70%    Bsmt_Full_Bath
##     20%           Bldg_Type
##     20%           Central_Air
##            90%    Lot_Area
##            89%    TotRms_AbvGrd
##            63%    Longitude
##            49%    Latitude
##            30%    Full_Bath
##            20%    Half_Bath
##            13%    Bsmt_Half_Bath
##             2%    Year_Sold
##
##
## Time: 0.0 secs

There is no formula method for cubist(); the predictors are specified as matrix or data frame, The outcome is a numeric vector.

There is a predict method for the model:

model_tree_pred <- predict(model_tree, test_pred)
## Test set RMSE
sqrt(mean((model_tree_pred - test_resp)^2))
## [1] 0.0751
## Test set R^2
cor(model_tree_pred, test_resp)^2
## [1] 0.82

## Ensembles By Committees

The Cubist model can also use a boosting-like scheme called committees where iterative model trees are created in sequence. The first tree follows the procedure described in the last section. Subsequent trees are created using adjusted versions to the training set outcome: if the model over-predicted a value, the response is adjusted downward for the next model (and so on, see this blog post). Unlike traditional boosting, stage weights for each committee are not used to average the predictions from each model tree; the final prediction is a simple average of the predictions from each model tree.

The committee option can be used to control number of model trees:

set.seed(1)
com_model <- cubist(x = train_pred, y = train_resp, committees = 3)
summary(com_model)
##
## Call:
## cubist.default(x = train_pred, y = train_resp, committees = 3)
##
##
## Cubist [Release 2.07 GPL Edition]  Mon Jul  1 21:23:49 2024
## ---------------------------------
##
##     Target attribute outcome'
##
## Read 2344 cases (18 attributes) from undefined.data
##
## Model 1:
##
##   Rule 1/1: [48 cases, mean 4.859286, range 4.106837 to 5.176091, est err 0.114526]
##
##     if
##  Neighborhood in {Old_Town, Sawyer_West, Iowa_DOT_and_Rail_Road}
##  Year_Built <= 1952
##  Central_Air = N
##  Gr_Liv_Area <= 1692
##     then
##  outcome = -352.195658 + 10.59 Latitude + 0.000362 Gr_Liv_Area
##            - 0.044 Year_Sold
##
##   Rule 1/2: [59 cases, mean 4.981545, range 4.594393 to 5.311754, est err 0.066423]
##
##     if
##  Neighborhood in {North_Ames, Edwards, Sawyer, Brookside, Crawford,
##                          South_and_West_of_Iowa_State_University}
##  Year_Built <= 1952
##  Central_Air = N
##  Gr_Liv_Area <= 1692
##     then
##  outcome = 3.738655 + 0.000281 Gr_Liv_Area + 8e-05 Total_Bsmt_SF
##            + 0.00047 Year_Built
##
##   Rule 1/3: [333 cases, mean 5.089450, range 4.69897 to 5.676693, est err 0.063499]
##
##     if
##  Neighborhood in {Old_Town, Edwards, Gilbert, Sawyer, Sawyer_West,
##                          South_and_West_of_Iowa_State_University}
##  Year_Built <= 1952
##  Central_Air = Y
##     then
##  outcome = -36.367569 + 0.000242 Gr_Liv_Area + 5.9e-05 Total_Bsmt_SF
##            + 0.00085 Year_Built + 0.94 Latitude - 0.01 TotRms_AbvGrd
##            + 0.036 Bsmt_Half_Bath + 0.006 Bsmt_Full_Bath + 5e-07 Lot_Area
##
##   Rule 1/4: [176 cases, mean 5.137743, range 4.788875 to 5.332842, est err 0.034310]
##
##     if
##  Neighborhood in {College_Creek, Edwards, Somerset, Northridge_Heights,
##                          Blueste, Landmark}
##  Year_Built > 1952
##  Total_Bsmt_SF <= 765
##  Gr_Liv_Area <= 1692
##     then
##  outcome = -28.661356 + 0.00333 Year_Built + 0.000188 Gr_Liv_Area
##            + 7.4e-05 Total_Bsmt_SF + 5.6e-06 Lot_Area + 0.64 Latitude
##            - 0.007 TotRms_AbvGrd
##
##   Rule 1/5: [35 cases, mean 5.150411, range 4.954243 to 5.361728, est err 0.050057]
##
##     if
##  Neighborhood in {North_Ames, Old_Town, Sawyer, Northwest_Ames, Crawford,
##                          Green_Hills}
##  Year_Built > 1952
##  Total_Bsmt_SF <= 765
##  Gr_Liv_Area <= 1692
##     then
##  outcome = 87.372832 + 0.000171 Total_Bsmt_SF + 8.5e-05 Gr_Liv_Area
##            + 0.00127 Year_Built - 2.02 Latitude + 3.8e-06 Lot_Area
##            - 0.005 TotRms_AbvGrd
##
##   Rule 1/6: [76 cases, mean 5.157218, range 4.973128 to 5.363612, est err 0.045520]
##
##     if
##  Neighborhood in {North_Ames, Crawford}
##  Year_Built <= 1952
##  Central_Air = Y
##  Gr_Liv_Area <= 1692
##     then
##  outcome = 141.774591 + 0.000129 Gr_Liv_Area - 3.26 Latitude
##            + 4.2e-06 Lot_Area + 0.037 Bsmt_Full_Bath + 9e-05 Year_Built
##            + 6e-06 Total_Bsmt_SF
##
##   Rule 1/7: [54 cases, mean 5.171031, range 4.79588 to 5.44832, est err 0.051527]
##
##     if
##  Bldg_Type in {Duplex, Twnhs}
##  Gr_Liv_Area > 1692
##     then
##  outcome = 2.31639 + 0.000232 Gr_Liv_Area + 0.00125 Year_Built
##            - 0.018 TotRms_AbvGrd + 6.1e-05 Total_Bsmt_SF + 9e-07 Lot_Area
##            + 0.01 Half_Bath + 0.007 Bsmt_Full_Bath
##
##   Rule 1/8: [473 cases, mean 5.192192, range 4.826075 to 5.509555, est err 0.037515]
##
##     if
##  Neighborhood in {North_Ames, College_Creek, Old_Town, Edwards, Gilbert,
##                          Sawyer, Northwest_Ames, Sawyer_West, Mitchell,
##  Total_Bsmt_SF > 765
##  Gr_Liv_Area <= 1692
##  Bsmt_Full_Bath > 0
##     then
##  outcome = 23.436857 + 0.00245 Year_Built + 0.000128 Gr_Liv_Area
##            + 0.000103 Total_Bsmt_SF + 2.5e-06 Lot_Area + 0.015 Full_Bath
##            + 0.25 Longitude + 0.003 TotRms_AbvGrd
##
##   Rule 1/9: [480 cases, mean 5.198712, range 4.778151 to 5.463805, est err 0.040757]
##
##     if
##  Year_Built > 1952
##  Total_Bsmt_SF > 765
##  Gr_Liv_Area <= 1692
##  Bsmt_Full_Bath <= 0
##     then
##  outcome = 18.268736 + 0.000174 Gr_Liv_Area + 0.00279 Year_Built
##            + 8.9e-05 Total_Bsmt_SF + 3.3e-06 Lot_Area
##            - 0.011 TotRms_AbvGrd + 0.016 Bsmt_Full_Bath + 0.26 Longitude
##            + 0.13 Latitude
##
##   Rule 1/10: [315 cases, mean 5.297982, range 4.905256 to 5.676693, est err 0.051186]
##
##     if
##  Neighborhood in {North_Ames, College_Creek, Old_Town, Edwards, Gilbert,
##                          Sawyer, Northwest_Ames, Sawyer_West, Mitchell,
##                          South_and_West_of_Iowa_State_University,
##  Bldg_Type in {OneFam, TwoFmCon, TwnhsE}
##  Year_Built <= 2004
##  Gr_Liv_Area > 1692
##     then
##  outcome = -28.023112 + 0.000157 Gr_Liv_Area + 0.0015 Year_Built
##            + 8.4e-05 Total_Bsmt_SF - 0.015 TotRms_AbvGrd + 0.03 Full_Bath
##            + 0.029 Half_Bath + 2.3e-06 Lot_Area - 0.32 Longitude
##            + 0.015 Bsmt_Full_Bath
##
##   Rule 1/11: [161 cases, mean 5.445366, range 5.142662 to 5.872156, est err 0.052062]
##
##     if
##  Neighborhood in {Somerset, Northridge_Heights, Brookside, Crawford,
##                          Northridge, Stone_Brook, Clear_Creek}
##  Bldg_Type in {OneFam, TwoFmCon, TwnhsE}
##  Year_Built <= 2004
##  Gr_Liv_Area > 1692
##     then
##  outcome = 2.344921 + 0.000151 Gr_Liv_Area + 0.00134 Year_Built
##            + 8.3e-05 Total_Bsmt_SF + 0.034 Bsmt_Full_Bath
##            - 0.004 TotRms_AbvGrd + 0.007 Half_Bath + 6e-07 Lot_Area
##
##   Rule 1/12: [275 cases, mean 5.452962, range 5.136721 to 5.872156, est err 0.051166]
##
##     if
##  Neighborhood in {Somerset, Northridge_Heights, Crawford, Northridge,
##                          Stone_Brook, Clear_Creek, Veenker, Blueste, Greens,
##                          Green_Hills}
##  Year_Built > 1952
##  Bsmt_Full_Bath > 0
##     then
##  outcome = 19.156714 + 0.000178 Gr_Liv_Area + 0.000159 Total_Bsmt_SF
##            + 0.00176 Year_Built + 1.4e-06 Lot_Area + 0.19 Longitude
##            + 0.007 Bsmt_Full_Bath - 0.002 TotRms_AbvGrd
##
##   Rule 1/13: [113 cases, mean 5.491452, range 5.281034 to 5.765619, est err 0.039038]
##
##     if
##  Year_Built > 2004
##  Total_Bsmt_SF <= 1907
##  Gr_Liv_Area > 1692
##     then
##  outcome = 25.674216 + 0.0097 Year_Built + 0.000152 Gr_Liv_Area
##            + 0.000109 Total_Bsmt_SF + 0.057 Bsmt_Full_Bath
##            + 0.68 Longitude + 0.56 Latitude
##
##   Rule 1/14: [35 cases, mean 5.602593, range 5.20412 to 5.786508, est err 0.077426]
##
##     if
##  Year_Built > 2004
##  Total_Bsmt_SF > 1907
##     then
##  outcome = -0.069641 - 9.9e-05 Gr_Liv_Area + 0.008 Bsmt_Full_Bath
##            + 0.14 Latitude + 0.001 TotRms_AbvGrd
##
## Model 2:
##
##   Rule 2/1: [66 cases, mean 4.924712, range 4.106837 to 5.266937, est err 0.114951]
##
##     if
##  Central_Air = N
##  Gr_Liv_Area <= 2035
##  Longitude > -93.62571
##     then
##  outcome = -580.059188 + 13.91 Latitude + 0.000299 Gr_Liv_Area
##
##   Rule 2/2: [144 cases, mean 4.960665, range 4.106837 to 5.311754, est err 0.099679]
##
##     if
##  Central_Air = N
##  Gr_Liv_Area <= 2035
##     then
##  outcome = 66.952321 + 0.000172 Gr_Liv_Area + 0.000122 Total_Bsmt_SF
##            - 0.031 Year_Sold
##
##   Rule 2/3: [314 cases, mean 5.090793, range 4.69897 to 5.676693, est err 0.062296]
##
##     if
##  Neighborhood in {Old_Town, Edwards, Gilbert, Sawyer_West,
##                          South_and_West_of_Iowa_State_University,
##  Year_Built <= 1968
##  Central_Air = Y
##     then
##  outcome = -67.722399 + 0.000207 Gr_Liv_Area - 0.01 TotRms_AbvGrd
##            + 0.78 Latitude + 0.024 Bsmt_Full_Bath - 0.42 Longitude
##            + 0.00024 Year_Built + 1.2e-05 Total_Bsmt_SF + 6e-07 Lot_Area
##            + 0.012 Bsmt_Half_Bath
##
##   Rule 2/4: [86 cases, mean 5.111280, range 4.788875 to 5.311754, est err 0.075818]
##
##     if
##  Bldg_Type = Duplex
##  Year_Built <= 1990
##     then
##  outcome = 3.017972 + 0.000162 Gr_Liv_Area + 0.0009 Year_Built
##            + 4.9e-05 Total_Bsmt_SF + 0.034 Bsmt_Full_Bath
##            - 0.011 TotRms_AbvGrd + 2.3e-06 Lot_Area
##            + 0.049 Bsmt_Half_Bath
##
##   Rule 2/5: [116 cases, mean 5.121052, range 4.851258 to 5.50515, est err 0.041411]
##
##     if
##  Neighborhood in {Old_Town, Edwards, Gilbert, Sawyer_West,
##                          South_and_West_of_Iowa_State_University,
##  Year_Built > 1968
##  Year_Built <= 1990
##     then
##  outcome = -112.729989 + 0.00697 Year_Built + 0.000177 Gr_Liv_Area
##            + 0.000109 Total_Bsmt_SF - 0.74 Longitude + 0.82 Latitude
##            - 0.002 TotRms_AbvGrd + 0.005 Bsmt_Full_Bath
##
##   Rule 2/6: [1037 cases, mean 5.200723, range 4.594393 to 5.676693, est err 0.047478]
##
##     if
##  Neighborhood in {North_Ames, College_Creek, Sawyer, Northwest_Ames,
##                          Mitchell, Brookside, Timberland, Blueste}
##     then
##  outcome = -11.037934 + 0.000128 Gr_Liv_Area + 8e-05 Total_Bsmt_SF
##            + 0.00095 Year_Built + 0.032 Bsmt_Full_Bath + 0.022 Full_Bath
##            + 0.046 Bsmt_Half_Bath + 1e-06 Lot_Area + 0.01 Half_Bath
##            - 0.15 Longitude
##
##   Rule 2/7: [265 cases, mean 5.260237, range 5.069113 to 5.449479, est err 0.025156]
##
##     if
##  Year_Built > 1990
##  Total_Bsmt_SF <= 952
##  Gr_Liv_Area <= 2035
##     then
##  outcome = -0.65428 + 0.000208 Gr_Liv_Area + 0.00279 Year_Built
##            + 0.03 Bsmt_Full_Bath
##
##   Rule 2/8: [141 cases, mean 5.292914, range 4.955928 to 5.585461, est err 0.064230]
##
##     if
##  Neighborhood in {Crawford, Stone_Brook, Clear_Creek, Veenker, Greens,
##                          Green_Hills}
##  Year_Built <= 1990
##     then
##  outcome = -13.943719 + 0.000192 Gr_Liv_Area + 0.00106 Year_Built
##            + 3.3e-05 Total_Bsmt_SF - 0.023 Full_Bath
##            + 0.019 Bsmt_Full_Bath + 0.026 Bsmt_Half_Bath
##            - 0.004 TotRms_AbvGrd + 0.012 Half_Bath + 9e-07 Lot_Area
##            - 0.18 Longitude
##
##   Rule 2/9: [133 cases, mean 5.323634, range 4.79588 to 5.676693, est err 0.094539]
##
##     if
##  Neighborhood in {North_Ames, Old_Town, Edwards, Gilbert, Sawyer,
##                          Northwest_Ames, Sawyer_West, Mitchell, Brookside,
##                          South_and_West_of_Iowa_State_University, Clear_Creek,
##  Gr_Liv_Area > 2035
##     then
##  outcome = -114.880872 + 2.8 Latitude + 0.00119 Year_Built
##            + 0.048 Full_Bath + 1.1e-05 Gr_Liv_Area + 8e-06 Total_Bsmt_SF
##            + 0.006 Bsmt_Full_Bath
##
##   Rule 2/10: [457 cases, mean 5.357634, range 4.926857 to 5.672098, est err 0.043722]
##
##     if
##  Year_Built > 1990
##  Total_Bsmt_SF > 952
##  Gr_Liv_Area <= 2035
##     then
##  outcome = 31.082771 + 0.00743 Year_Built + 0.000272 Gr_Liv_Area
##            + 0.000144 Total_Bsmt_SF + 0.059 Bsmt_Full_Bath
##            - 0.015 TotRms_AbvGrd + 3.9e-06 Lot_Area + 0.44 Longitude
##
##   Rule 2/11: [82 cases, mean 5.482795, range 5.281034 to 5.872156, est err 0.050959]
##
##     if
##  Neighborhood in {College_Creek, Somerset, Northridge_Heights, Crawford,
##                          Timberland, Northridge, Stone_Brook}
##  Year_Built <= 2002
##  Gr_Liv_Area > 2035
##     then
##  outcome = -1.964556 + 0.000126 Gr_Liv_Area + 3.8e-06 Lot_Area
##            + 0.00018 Year_Built + 0.16 Latitude + 0.004 Bsmt_Full_Bath
##
##   Rule 2/12: [78 cases, mean 5.585224, range 5.380211 to 5.788875, est err 0.048668]
##
##     if
##  Neighborhood in {College_Creek, Somerset, Northridge_Heights, Crawford,
##                          Timberland, Northridge, Stone_Brook}
##  Year_Built > 2002
##  Gr_Liv_Area > 2035
##     then
##  outcome = 137.506637 + 0.01229 Year_Built + 9.6e-05 Gr_Liv_Area
##            + 1.91 Longitude + 0.000107 Total_Bsmt_SF
##            + 0.044 Bsmt_Full_Bath + 0.52 Latitude + 9e-07 Lot_Area
##
## Model 3:
##
##   Rule 3/1: [33 cases, mean 4.765631, range 4.106837 to 5.041393, est err 0.159903]
##
##     if
##  Central_Air = N
##  Gr_Liv_Area <= 845
##     then
##  outcome = -391.954369 + 0.000768 Gr_Liv_Area - 4.23 Longitude
##            + 7e-05 Year_Built
##
##   Rule 3/2: [104 cases, mean 4.928864, range 4.106837 to 5.175802, est err 0.091334]
##
##     if
##  Gr_Liv_Area <= 845
##     then
##  outcome = -1.229338 + 0.000359 Total_Bsmt_SF + 2.31e-05 Lot_Area
##            + 0.00297 Year_Built + 5.1e-05 Gr_Liv_Area
##
##   Rule 3/3: [215 cases, mean 5.123382, range 4.740363 to 5.44832, est err 0.057230]
##
##     if
##  Bldg_Type in {TwoFmCon, Duplex, Twnhs}
##  Year_Built <= 2005
##  Gr_Liv_Area > 845
##     then
##  outcome = 41.243193 + 0.000147 Gr_Liv_Area + 0.00201 Year_Built
##            + 0.056 Bsmt_Full_Bath + 0.48 Longitude
##            + 2.6e-05 Total_Bsmt_SF - 0.006 TotRms_AbvGrd + 8e-07 Lot_Area
##            + 0.11 Latitude + 0.004 Half_Bath
##
##   Rule 3/4: [248 cases, mean 5.136273, range 4.79588 to 5.676693, est err 0.074758]
##
##     if
##  Year_Built <= 1951
##  Total_Bsmt_SF > 800
##  Gr_Liv_Area > 845
##     then
##  outcome = 4.685451 + 0.000248 Gr_Liv_Area + 0.114 Half_Bath
##            - 0.029 TotRms_AbvGrd + 5e-06 Lot_Area + 0.0001 Year_Built
##            + 6e-06 Total_Bsmt_SF
##
##   Rule 3/5: [438 cases, mean 5.150761, range 4.60206 to 5.580925, est err 0.057916]
##
##     if
##  Bldg_Type in {OneFam, TwnhsE}
##  Total_Bsmt_SF <= 800
##  Gr_Liv_Area > 845
##     then
##  outcome = 2.032241 + 0.000165 Gr_Liv_Area + 0.000116 Total_Bsmt_SF
##            + 0.00142 Year_Built + 0.033 Full_Bath - 0.003 TotRms_AbvGrd
##            + 0.008 Bsmt_Full_Bath + 6e-07 Lot_Area + 0.006 Half_Bath
##
##   Rule 3/6: [1358 cases, mean 5.252755, range 4.117271 to 5.872156, est err 0.042818]
##
##     if
##  Bldg_Type in {OneFam, TwnhsE}
##  Year_Built > 1951
##  Year_Built <= 2005
##     then
##  outcome = 40.010366 + 0.000187 Gr_Liv_Area + 0.00307 Year_Built
##            + 9.9e-05 Total_Bsmt_SF + 0.038 Bsmt_Full_Bath
##            - 0.011 TotRms_AbvGrd + 2.5e-06 Lot_Area + 0.44 Longitude
##
##   Rule 3/7: [235 cases, mean 5.400186, range 4.926857 to 5.765619, est err 0.047093]
##
##     if
##  Year_Built > 2005
##  Total_Bsmt_SF <= 2006
##     then
##  outcome = -3.631741 + 0.00424 Year_Built + 0.000131 Gr_Liv_Area
##            + 0.000125 Total_Bsmt_SF + 0.075 Bsmt_Full_Bath
##            + 5.4e-06 Lot_Area + 0.011 TotRms_AbvGrd
##
##   Rule 3/8: [20 cases, mean 5.583736, range 5.20412 to 5.786508, est err 0.103229]
##
##     if
##  Year_Built > 2005
##  Total_Bsmt_SF > 2006
##     then
##  outcome = 4.772929 - 0.000158 Gr_Liv_Area + 0.00059 Year_Built
##            + 0.002 TotRms_AbvGrd + 4e-06 Total_Bsmt_SF
##
##
## Evaluation on training data (2344 cases):
##
##     Average  |error|           0.051966
##     Relative |error|               0.38
##     Correlation coefficient        0.91
##
##
##  Attribute usage:
##    Conds  Model
##
##     70%    96%    Year_Built
##     52%   100%    Gr_Liv_Area
##     47%           Neighborhood
##     36%    94%    Total_Bsmt_SF
##     32%           Bldg_Type
##     15%    83%    Bsmt_Full_Bath
##     13%           Central_Air
##            66%    Longitude
##            87%    Lot_Area
##            73%    TotRms_AbvGrd
##            32%    Half_Bath
##            31%    Full_Bath
##            28%    Latitude
##            23%    Bsmt_Half_Bath
##             2%    Year_Sold
##
##
## Time: 0.1 secs

For this model:

com_pred <- predict(com_model, test_pred)
## RMSE
sqrt(mean((com_pred - test_resp)^2))
## [1] 0.0708
## R^2
cor(com_pred, test_resp)^2
## [1] 0.839

## Instance-Based Corrections

Another innovation in Cubist using nearest-neighbors to adjust the predictions from the rule-based model. First, a model tree (with or without committees) is created. Once a sample is predicted by this model, Cubist can find it’s nearest neighbors and determine the average of these training set points. See Quinlan (1993a) for the details of the adjustment as well as this blog post.

The development of rules and committees is independent of the choice of using instances. The original C code allowed the program to choose whether to use instances, not use them or let the program decide. Our approach is to build a model with the cubist() function that is ignorant to the decision about instances. When samples are predicted, the argument neighbors can be used to adjust the rule-based model predictions (or not).

We can add instances to the previously fit committee model:

inst_pred <- predict(com_model, test_pred, neighbors = 5)
## RMSE
sqrt(mean((inst_pred - test_resp)^2))
## [1] 0.0688
## R^2
cor(inst_pred, test_resp)^2
## [1] 0.848

Note that the previous models used the implicit default of neighbors = 0 for their predictions.

It may also be useful to see how the different models fit a single predictor. Here is the test set data for a model with one predictor (Gr_Liv_Area), 100 committees, and various values of neighbors:

After the initial use of the instance-based correction, there is very little change in the mainstream of the data.

## Model tuning

R modeling packages such as caret, tidymodels, and mlr3 can be used to tune the model. See the examples here for more details.

It should be noted that this variable importance measure does not capture the influence of the predictors when using the instance-based correction.

## Extracting Rules

Rules from a Cubist model can be viewed using summary as follows:

summary(model_tree)
##
## Call:
## cubist.default(x = train_pred, y = train_resp)
##
##
## Cubist [Release 2.07 GPL Edition]  Mon Jul  1 21:23:48 2024
## ---------------------------------
##
##     Target attribute outcome'
##
## Read 2344 cases (18 attributes) from undefined.data
##
## Model:
##
##   Rule 1: [48 cases, mean 4.859286, range 4.106837 to 5.176091, est err 0.114526]
##
##     if
##  Neighborhood in {Old_Town, Sawyer_West, Iowa_DOT_and_Rail_Road}
##  Year_Built <= 1952
##  Central_Air = N
##  Gr_Liv_Area <= 1692
##     then
##  outcome = -352.195658 + 10.59 Latitude + 0.000362 Gr_Liv_Area
##            - 0.044 Year_Sold
##
##   Rule 2: [59 cases, mean 4.981545, range 4.594393 to 5.311754, est err 0.066423]
##
##     if
##  Neighborhood in {North_Ames, Edwards, Sawyer, Brookside, Crawford,
##                          South_and_West_of_Iowa_State_University}
##  Year_Built <= 1952
##  Central_Air = N
##  Gr_Liv_Area <= 1692
##     then
##  outcome = 3.738655 + 0.000281 Gr_Liv_Area + 8e-05 Total_Bsmt_SF
##            + 0.00047 Year_Built
##
##   Rule 3: [333 cases, mean 5.089450, range 4.69897 to 5.676693, est err 0.063499]
##
##     if
##  Neighborhood in {Old_Town, Edwards, Gilbert, Sawyer, Sawyer_West,
##                          South_and_West_of_Iowa_State_University}
##  Year_Built <= 1952
##  Central_Air = Y
##     then
##  outcome = -36.367569 + 0.000242 Gr_Liv_Area + 5.9e-05 Total_Bsmt_SF
##            + 0.00085 Year_Built + 0.94 Latitude - 0.01 TotRms_AbvGrd
##            + 0.036 Bsmt_Half_Bath + 0.006 Bsmt_Full_Bath + 5e-07 Lot_Area
##
##   Rule 4: [176 cases, mean 5.137743, range 4.788875 to 5.332842, est err 0.034310]
##
##     if
##  Neighborhood in {College_Creek, Edwards, Somerset, Northridge_Heights,
##                          Blueste, Landmark}
##  Year_Built > 1952
##  Total_Bsmt_SF <= 765
##  Gr_Liv_Area <= 1692
##     then
##  outcome = -28.661356 + 0.00333 Year_Built + 0.000188 Gr_Liv_Area
##            + 7.4e-05 Total_Bsmt_SF + 5.6e-06 Lot_Area + 0.64 Latitude
##            - 0.007 TotRms_AbvGrd
##
##   Rule 5: [35 cases, mean 5.150411, range 4.954243 to 5.361728, est err 0.050057]
##
##     if
##  Neighborhood in {North_Ames, Old_Town, Sawyer, Northwest_Ames, Crawford,
##                          Green_Hills}
##  Year_Built > 1952
##  Total_Bsmt_SF <= 765
##  Gr_Liv_Area <= 1692
##     then
##  outcome = 87.372832 + 0.000171 Total_Bsmt_SF + 8.5e-05 Gr_Liv_Area
##            + 0.00127 Year_Built - 2.02 Latitude + 3.8e-06 Lot_Area
##            - 0.005 TotRms_AbvGrd
##
##   Rule 6: [76 cases, mean 5.157218, range 4.973128 to 5.363612, est err 0.045520]
##
##     if
##  Neighborhood in {North_Ames, Crawford}
##  Year_Built <= 1952
##  Central_Air = Y
##  Gr_Liv_Area <= 1692
##     then
##  outcome = 141.774591 + 0.000129 Gr_Liv_Area - 3.26 Latitude
##            + 4.2e-06 Lot_Area + 0.037 Bsmt_Full_Bath + 9e-05 Year_Built
##            + 6e-06 Total_Bsmt_SF
##
##   Rule 7: [54 cases, mean 5.171031, range 4.79588 to 5.44832, est err 0.051527]
##
##     if
##  Bldg_Type in {Duplex, Twnhs}
##  Gr_Liv_Area > 1692
##     then
##  outcome = 2.31639 + 0.000232 Gr_Liv_Area + 0.00125 Year_Built
##            - 0.018 TotRms_AbvGrd + 6.1e-05 Total_Bsmt_SF + 9e-07 Lot_Area
##            + 0.01 Half_Bath + 0.007 Bsmt_Full_Bath
##
##   Rule 8: [473 cases, mean 5.192192, range 4.826075 to 5.509555, est err 0.037515]
##
##     if
##  Neighborhood in {North_Ames, College_Creek, Old_Town, Edwards, Gilbert,
##                          Sawyer, Northwest_Ames, Sawyer_West, Mitchell,
##  Total_Bsmt_SF > 765
##  Gr_Liv_Area <= 1692
##  Bsmt_Full_Bath > 0
##     then
##  outcome = 23.436857 + 0.00245 Year_Built + 0.000128 Gr_Liv_Area
##            + 0.000103 Total_Bsmt_SF + 2.5e-06 Lot_Area + 0.015 Full_Bath
##            + 0.25 Longitude + 0.003 TotRms_AbvGrd
##
##   Rule 9: [480 cases, mean 5.198712, range 4.778151 to 5.463805, est err 0.040757]
##
##     if
##  Year_Built > 1952
##  Total_Bsmt_SF > 765
##  Gr_Liv_Area <= 1692
##  Bsmt_Full_Bath <= 0
##     then
##  outcome = 18.268736 + 0.000174 Gr_Liv_Area + 0.00279 Year_Built
##            + 8.9e-05 Total_Bsmt_SF + 3.3e-06 Lot_Area
##            - 0.011 TotRms_AbvGrd + 0.016 Bsmt_Full_Bath + 0.26 Longitude
##            + 0.13 Latitude
##
##   Rule 10: [315 cases, mean 5.297982, range 4.905256 to 5.676693, est err 0.051186]
##
##     if
##  Neighborhood in {North_Ames, College_Creek, Old_Town, Edwards, Gilbert,
##                          Sawyer, Northwest_Ames, Sawyer_West, Mitchell,
##                          South_and_West_of_Iowa_State_University,
##  Bldg_Type in {OneFam, TwoFmCon, TwnhsE}
##  Year_Built <= 2004
##  Gr_Liv_Area > 1692
##     then
##  outcome = -28.023112 + 0.000157 Gr_Liv_Area + 0.0015 Year_Built
##            + 8.4e-05 Total_Bsmt_SF - 0.015 TotRms_AbvGrd + 0.03 Full_Bath
##            + 0.029 Half_Bath + 2.3e-06 Lot_Area - 0.32 Longitude
##            + 0.015 Bsmt_Full_Bath
##
##   Rule 11: [161 cases, mean 5.445366, range 5.142662 to 5.872156, est err 0.052062]
##
##     if
##  Neighborhood in {Somerset, Northridge_Heights, Brookside, Crawford,
##                          Northridge, Stone_Brook, Clear_Creek}
##  Bldg_Type in {OneFam, TwoFmCon, TwnhsE}
##  Year_Built <= 2004
##  Gr_Liv_Area > 1692
##     then
##  outcome = 2.344921 + 0.000151 Gr_Liv_Area + 0.00134 Year_Built
##            + 8.3e-05 Total_Bsmt_SF + 0.034 Bsmt_Full_Bath
##            - 0.004 TotRms_AbvGrd + 0.007 Half_Bath + 6e-07 Lot_Area
##
##   Rule 12: [275 cases, mean 5.452962, range 5.136721 to 5.872156, est err 0.051166]
##
##     if
##  Neighborhood in {Somerset, Northridge_Heights, Crawford, Northridge,
##                          Stone_Brook, Clear_Creek, Veenker, Blueste, Greens,
##                          Green_Hills}
##  Year_Built > 1952
##  Bsmt_Full_Bath > 0
##     then
##  outcome = 19.156714 + 0.000178 Gr_Liv_Area + 0.000159 Total_Bsmt_SF
##            + 0.00176 Year_Built + 1.4e-06 Lot_Area + 0.19 Longitude
##            + 0.007 Bsmt_Full_Bath - 0.002 TotRms_AbvGrd
##
##   Rule 13: [113 cases, mean 5.491452, range 5.281034 to 5.765619, est err 0.039038]
##
##     if
##  Year_Built > 2004
##  Total_Bsmt_SF <= 1907
##  Gr_Liv_Area > 1692
##     then
##  outcome = 25.674216 + 0.0097 Year_Built + 0.000152 Gr_Liv_Area
##            + 0.000109 Total_Bsmt_SF + 0.057 Bsmt_Full_Bath
##            + 0.68 Longitude + 0.56 Latitude
##
##   Rule 14: [35 cases, mean 5.602593, range 5.20412 to 5.786508, est err 0.077426]
##
##     if
##  Year_Built > 2004
##  Total_Bsmt_SF > 1907
##     then
##  outcome = -0.069641 - 9.9e-05 Gr_Liv_Area + 0.008 Bsmt_Full_Bath
##            + 0.14 Latitude + 0.001 TotRms_AbvGrd
##
##
## Evaluation on training data (2344 cases):
##
##     Average  |error|           0.053913
##     Relative |error|               0.39
##     Correlation coefficient        0.90
##
##
##  Attribute usage:
##    Conds  Model
##
##     80%    97%    Year_Built
##     76%   100%    Gr_Liv_Area
##     74%           Neighborhood
##     50%    97%    Total_Bsmt_SF
##     47%    70%    Bsmt_Full_Bath
##     20%           Bldg_Type
##     20%           Central_Air
##            90%    Lot_Area
##            89%    TotRms_AbvGrd
##            63%    Longitude
##            49%    Latitude
##            30%    Full_Bath
##            20%    Half_Bath
##            13%    Bsmt_Half_Bath
##             2%    Year_Sold
##
##
## Time: 0.0 secs

The tidy() function in the rules package returns rules in a tibble (an extension of data frames) with one row per rule. The tibble provides information about the rule and can be used to programatically extra data from the model. For example:

library(rules)

rule_df <- tidy(model_tree)

rule_df
## # A tibble: 14 × 5
##    committee rule_num rule                                    estimate statistic
##        <int>    <int> <chr>                                   <list>   <list>
##  1         1        1 ( Central_Air == 'N' ) & ( Neighborhoo… <tibble> <tibble>
##  2         1        2 ( Central_Air == 'N' ) & ( Neighborhoo… <tibble> <tibble>
##  3         1        3 ( Year_Built <= 1952 ) & ( Central_Air… <tibble> <tibble>
##  4         1        4 ( Total_Bsmt_SF <= 765 ) & ( Year_Buil… <tibble> <tibble>
##  5         1        5 ( Total_Bsmt_SF <= 765 ) & ( Neighborh… <tibble> <tibble>
##  6         1        6 ( Neighborhood  %in% c( 'North_Ames','… <tibble> <tibble>
##  7         1        7 ( Bldg_Type  %in% c( 'Duplex','Twnhs' … <tibble> <tibble>
##  8         1        8 ( Bsmt_Full_Bath > 0 ) & ( Gr_Liv_Area… <tibble> <tibble>
##  9         1        9 ( Bsmt_Full_Bath <= 0 ) & ( Gr_Liv_Are… <tibble> <tibble>
## 10         1       10 ( Gr_Liv_Area > 1692 ) & ( Neighborhoo… <tibble> <tibble>
## 11         1       11 ( Neighborhood  %in% c( 'Somerset','No… <tibble> <tibble>
## 12         1       12 ( Neighborhood  %in% c( 'Somerset','No… <tibble> <tibble>
## 13         1       13 ( Year_Built > 2004 ) & ( Gr_Liv_Area … <tibble> <tibble>
## 14         1       14 ( Total_Bsmt_SF > 1907 ) & ( Year_Buil… <tibble> <tibble>
rule_df$estimate[[1]] ## # A tibble: 4 × 2 ## term estimate ## <chr> <dbl> ## 1 (Intercept) -352. ## 2 Gr_Liv_Area 0.000362 ## 3 Year_Sold -0.044 ## 4 Latitude 10.6 rule_df$statistic[[1]]
## # A tibble: 1 × 6
##   num_conditions coverage  mean   min   max error
##            <dbl>    <dbl> <dbl> <dbl> <dbl> <dbl>
## 1              4       48  4.86  4.11  5.18 0.115

The rule column can be converted to an R expression that can be used to pull data used by that rule. For example, for the seventh rule:

# Text
rule_7 <- rule_df\$rule[7]

# Convert to an expression
rule_7 <- rlang::parse_expr(rule_7)
rule_7
## (Bldg_Type %in% c("Duplex", "Twnhs")) & (Gr_Liv_Area > 1692)
# Use in a dplyr filter:
nrow(train_pred)
## [1] 2344
library(dplyr)

train_pred %>% filter(!!rule_7) %>% nrow()
## [1] 54

## Variable Importance

The summary() method for Cubist shows the usage of each variable in either the rule conditions or the (terminal) linear model. In actuality, many more linear models are used in prediction that are shown in the output. Because of this, the variable usage statistics shown at the end of the output of the summary() function will probably be inconsistent with the rules also shown in the output. At each split of the tree, Cubist saves a linear model (after feature selection) that is allowed to have terms for each variable used in the current split or any split above it. Quinlan (1992) discusses a smoothing algorithm where each model prediction is a linear combination of the parent and child model along the tree. As such, the final prediction is a function of all the linear models from the initial node to the terminal node. The percentages shown in the Cubist output reflects all the models involved in prediction (as opposed to the terminal models shown in the output).

The raw usage statistics are contained in a data frame called usage in the cubist object.

The caret and vip packages have general variable importance functions caret::varImp() and vip::vi(). When using this function on a cubist argument, the variable importance is a linear combination of the usage in the rule conditions and the model.

For example, to compute the scores:

caret::varImp(model_tree)

# or

vip::vi(model_tree)

## Exporting the Model Using the RuleQuest file format

As previously mentioned, this code is a port of the command-line C code. To run the C code, the training set data must be converted to a specific file format as detailed on the RuleQuest website. Two files are created. The file.data file is a header-less, comma delimited version of the data (the file part is a name given by the user). The file.names file provides information about the columns (eg. levels for categorical data and so on). After running the C program, another text file called file.models, which contains the information needed for prediction.

Once a model has been built with the R cubist package, the exportCubistFiles can be used to create the .data, .names and .model files so that the same model can be run at the command-line.

## Current Limitations

There are a few features in the C code that are not yet operational in the R package:

• only continuous and categorical predictors can be used (the original source code allows for other data types)
• there is an option to let the C code decide on using instances or not. The choice is more explicit in this package
• non-standard predictor names are not currently checked/fixed
• the C` code supports binning of predictors