Cubist models

Function	Works
`tidypredict_fit()`, `tidypredict_sql()`, `parse_model()`	✔
`tidypredict_to_column()`	✔
`tidypredict_test()`	✔
`tidypredict_interval()`, `tidypredict_sql_interval()`	✗
`parsnip`	✗

`tidypredict_` functions

library(Cubist)
data("BostonHousing", package =  "mlbench")

model <- Cubist::cubist(x = BostonHousing[, -14], y = BostonHousing$medv, committees = 3)

Create the R formula

tidypredict_fit(model)
#> (ifelse(nox >= 0.668, -1.11 + crim * -0.02 + nox * 21.4 + rm * 
#>     0.1 + age * -0.003 + dis * 2.93 + ptratio * -0.13 + b * 0.008 + 
#>     lstat * -0.33, 0) + ifelse(lstat >= 9.59 & nox < 0.668, 23.57 + 
#>     crim * 0.05 + nox * -5.2 + rm * 3.1 + age * -0.048 + dis * 
#>     -0.81 + rad * 0.02 + tax * -0.0041 + ptratio * -0.71 + b * 
#>     0.01 + lstat * -0.15, 0) + ifelse(lstat < 9.59 & rm < 6.226, 
#>     1.18 + crim * 3.83 + rm * 4.3 + age * -0.06 + dis * -0.09 + 
#>         tax * -0.003 + ptratio * -0.08 + lstat * -0.11, 0) + 
#>     ifelse(lstat < 9.59 & rm >= 6.226, -4.71 + crim * 2.22 + 
#>         zn * 0.008 + nox * -1.7 + rm * 9.2 + age * -0.04 + dis * 
#>         -0.71 + rad * 0.03 + tax * -0.0182 + ptratio * -0.72 + 
#>         lstat * -0.83, 0) + ifelse(dis < 1.755 & lstat >= 5.12, 
#>     122.32 + crim * -0.29 + nox * -21.6 + rm * -3 + dis * -30.88 + 
#>         rad * 0.02 + tax * -0.001 + b * -0.023 + lstat * -0.73, 
#>     0) + ifelse(rm < 6.545 & lstat >= 5.12, 27.8 + crim * -0.16 + 
#>     zn * 0.007 + nox * -3.9 + rm * 2 + age * -0.035 + dis * -0.7 + 
#>     rad * 0.28 + tax * -0.0135 + ptratio * -0.6 + b * 0.013 + 
#>     lstat * -0.25, 0) + ifelse(rm >= 6.545 & lstat >= 5.12, 22.21 + 
#>     crim * -0.04 + zn * 0.01 + indus * -0.02 + nox * -4 + rm * 
#>     4.7 + dis * -0.34 + rad * 0.11 + tax * -0.0248 + ptratio * 
#>     -0.9 + b * 0.002 + lstat * -0.1, 0) + ifelse(lstat < 5.12 & 
#>     rm < 8.034, -71.95 + rm * 17 + age * -0.06 + tax * -0.0112 + 
#>     ptratio * -0.48 + lstat * -0.03, 0) + ifelse(rm >= 8.034 & 
#>     dis >= 3.199, -32.79 + crim * -0.01 + zn * 0.005 + nox * 
#>     -1.8 + rm * 12.9 + age * -0.117 + dis * -0.15 + rad * 0.04 + 
#>     tax * -0.0246 + ptratio * -1.05 + lstat * -0.04, 0) + ifelse(lstat < 
#>     5.12 & dis < 3.199, 53.41 + rm * 1.6 + dis * -7.16 + tax * 
#>     0.0088 + lstat * -0.68, 0) + ifelse(nox >= 0.668, -36.31 + 
#>     crim * 0.08 + nox * 48.4 + dis * 7.52 + b * 0.01 + lstat * 
#>     -0.24, 0) + ifelse(lstat >= 9.53 & nox < 0.668, 28.04 + nox * 
#>     -4.8 + rm * 2.9 + age * -0.051 + dis * -0.86 + rad * 0.01 + 
#>     tax * -0.0019 + ptratio * -0.72 + lstat * -0.12, 0) + ifelse(lstat < 
#>     9.53, -26.05 + crim * 0.89 + nox * -2.3 + rm * 9.6 + dis * 
#>     -0.17 + rad * 0.02 + tax * -0.0055 + ptratio * -0.12 + b * 
#>     0.001 + lstat * -0.74, 0) + ifelse(lstat < 9.53 & dis < 2.64, 
#>     136.67 + crim * 7.2 + nox * -96.6 + rm * 1.1 + tax * -0.0033 + 
#>         ptratio * -3.31 + lstat * -0.1, 0))/3

SQL output example

tidypredict_sql(model, dbplyr::simulate_odbc())
#> <SQL> CASE WHEN (`nox` >= 0.668) THEN (-1.11 + `crim` * -0.02 + `nox` * 21.4 + `rm` * 0.1 + `age` * -0.003 + `dis` * 2.93 + `ptratio` * -0.13 + `b` * 0.008 + `lstat` * -0.33) WHEN NOT(`nox` >= 0.668) THEN (0.0) END + CASE WHEN (`lstat` >= 9.59 AND `nox` < 0.668) THEN (23.57 + `crim` * 0.05 + `nox` * -5.2 + `rm` * 3.1 + `age` * -0.048 + `dis` * -0.81 + `rad` * 0.02 + `tax` * -0.0041 + `ptratio` * -0.71 + `b` * 0.01 + `lstat` * -0.15) WHEN NOT(`lstat` >= 9.59 AND `nox` < 0.668) THEN (0.0) END + CASE WHEN (`lstat` < 9.59 AND `rm` < 6.226) THEN (1.18 + `crim` * 3.83 + `rm` * 4.3 + `age` * -0.06 + `dis` * -0.09 + `tax` * -0.003 + `ptratio` * -0.08 + `lstat` * -0.11) WHEN NOT(`lstat` < 9.59 AND `rm` < 6.226) THEN (0.0) END + CASE WHEN (`lstat` < 9.59 AND `rm` >= 6.226) THEN (-4.71 + `crim` * 2.22 + `zn` * 0.008 + `nox` * -1.7 + `rm` * 9.2 + `age` * -0.04 + `dis` * -0.71 + `rad` * 0.03 + `tax` * -0.0182 + `ptratio` * -0.72 + `lstat` * -0.83) WHEN NOT(`lstat` < 9.59 AND `rm` >= 6.226) THEN (0.0) END + CASE WHEN (`dis` < 1.755 AND `lstat` >= 5.12) THEN (122.32 + `crim` * -0.29 + `nox` * -21.6 + `rm` * -3.0 + `dis` * -30.88 + `rad` * 0.02 + `tax` * -0.001 + `b` * -0.023 + `lstat` * -0.73) WHEN NOT(`dis` < 1.755 AND `lstat` >= 5.12) THEN (0.0) END + CASE WHEN (`rm` < 6.545 AND `lstat` >= 5.12) THEN (27.8 + `crim` * -0.16 + `zn` * 0.007 + `nox` * -3.9 + `rm` * 2.0 + `age` * -0.035 + `dis` * -0.7 + `rad` * 0.28 + `tax` * -0.0135 + `ptratio` * -0.6 + `b` * 0.013 + `lstat` * -0.25) WHEN NOT(`rm` < 6.545 AND `lstat` >= 5.12) THEN (0.0) END + CASE WHEN (`rm` >= 6.545 AND `lstat` >= 5.12) THEN (22.21 + `crim` * -0.04 + `zn` * 0.01 + `indus` * -0.02 + `nox` * -4.0 + `rm` * 4.7 + `dis` * -0.34 + `rad` * 0.11 + `tax` * -0.0248 + `ptratio` * -0.9 + `b` * 0.002 + `lstat` * -0.1) WHEN NOT(`rm` >= 6.545 AND `lstat` >= 5.12) THEN (0.0) END + CASE WHEN (`lstat` < 5.12 AND `rm` < 8.034) THEN (-71.95 + `rm` * 17.0 + `age` * -0.06 + `tax` * -0.0112 + `ptratio` * -0.48 + `lstat` * -0.03) WHEN NOT(`lstat` < 5.12 AND `rm` < 8.034) THEN (0.0) END + CASE WHEN (`rm` >= 8.034 AND `dis` >= 3.199) THEN (-32.79 + `crim` * -0.01 + `zn` * 0.005 + `nox` * -1.8 + `rm` * 12.9 + `age` * -0.117 + `dis` * -0.15 + `rad` * 0.04 + `tax` * -0.0246 + `ptratio` * -1.05 + `lstat` * -0.04) WHEN NOT(`rm` >= 8.034 AND `dis` >= 3.199) THEN (0.0) END + CASE WHEN (`lstat` < 5.12 AND `dis` < 3.199) THEN (53.41 + `rm` * 1.6 + `dis` * -7.16 + `tax` * 0.0088 + `lstat` * -0.68) WHEN NOT(`lstat` < 5.12 AND `dis` < 3.199) THEN (0.0) END + CASE WHEN (`nox` >= 0.668) THEN (-36.31 + `crim` * 0.08 + `nox` * 48.4 + `dis` * 7.52 + `b` * 0.01 + `lstat` * -0.24) WHEN NOT(`nox` >= 0.668) THEN (0.0) END + CASE WHEN (`lstat` >= 9.53 AND `nox` < 0.668) THEN (28.04 + `nox` * -4.8 + `rm` * 2.9 + `age` * -0.051 + `dis` * -0.86 + `rad` * 0.01 + `tax` * -0.0019 + `ptratio` * -0.72 + `lstat` * -0.12) WHEN NOT(`lstat` >= 9.53 AND `nox` < 0.668) THEN (0.0) END + CASE WHEN (`lstat` < 9.53) THEN (-26.05 + `crim` * 0.89 + `nox` * -2.3 + `rm` * 9.6 + `dis` * -0.17 + `rad` * 0.02 + `tax` * -0.0055 + `ptratio` * -0.12 + `b` * 0.001 + `lstat` * -0.74) WHEN NOT(`lstat` < 9.53) THEN (0.0) END + CASE WHEN (`lstat` < 9.53 AND `dis` < 2.64) THEN (136.67 + `crim` * 7.2 + `nox` * -96.6 + `rm` * 1.1 + `tax` * -0.0033 + `ptratio` * -3.31 + `lstat` * -0.1) WHEN NOT(`lstat` < 9.53 AND `dis` < 2.64) THEN (0.0) END / 3.0

Add the prediction to the original table

library(dplyr)

BostonHousing %>%
  tidypredict_to_column(model) %>%
  glimpse()
#> Rows: 506
#> Columns: 15
#> $ crim    <dbl> 0.00632, 0.02731, 0.02729, 0.03237, 0.06905, 0.02985, 0.08829…
#> $ zn      <dbl> 18.0, 0.0, 0.0, 0.0, 0.0, 0.0, 12.5, 12.5, 12.5, 12.5, 12.5, …
#> $ indus   <dbl> 2.31, 7.07, 7.07, 2.18, 2.18, 2.18, 7.87, 7.87, 7.87, 7.87, 7…
#> $ chas    <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
#> $ nox     <dbl> 0.538, 0.469, 0.469, 0.458, 0.458, 0.458, 0.524, 0.524, 0.524…
#> $ rm      <dbl> 6.575, 6.421, 7.185, 6.998, 7.147, 6.430, 6.012, 6.172, 5.631…
#> $ age     <dbl> 65.2, 78.9, 61.1, 45.8, 54.2, 58.7, 66.6, 96.1, 100.0, 85.9, …
#> $ dis     <dbl> 4.0900, 4.9671, 4.9671, 6.0622, 6.0622, 6.0622, 5.5605, 5.950…
#> $ rad     <dbl> 1, 2, 2, 3, 3, 3, 5, 5, 5, 5, 5, 5, 5, 4, 4, 4, 4, 4, 4, 4, 4…
#> $ tax     <dbl> 296, 242, 242, 222, 222, 222, 311, 311, 311, 311, 311, 311, 3…
#> $ ptratio <dbl> 15.3, 17.8, 17.8, 18.7, 18.7, 18.7, 15.2, 15.2, 15.2, 15.2, 1…
#> $ b       <dbl> 396.90, 396.90, 392.83, 394.63, 396.90, 394.12, 395.60, 396.9…
#> $ lstat   <dbl> 4.98, 9.14, 4.03, 2.94, 5.33, 5.21, 12.43, 19.15, 29.93, 17.1…
#> $ medv    <dbl> 24.0, 21.6, 34.7, 33.4, 36.2, 28.7, 22.9, 27.1, 16.5, 18.9, 1…
#> $ fit     <dbl> 27.50665, 22.71805, 34.78128, 33.19372, 31.93653, 25.03739, 2…

Confirm that tidypredict results match to the model’s predict() results

tidypredict_test(model, BostonHousing)
#> tidypredict test results
#> Difference threshold: 1e-12
#> 
#> Fitted records above the threshold: 506
#> 
#> Fit max  difference:
#> Lower max difference:
#> Upper max difference:2.62966510913086

Cubist models

tidypredict_ functions

Parse model spec

`tidypredict_` functions