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Random Forest: Which attribute levels matter most?

Source: vignettes/forest.Rmd
forest.Rmd

When to use

Always fit method = "forest" first. It is non-parametric, makes no behavioural assumptions, and gives you the importance hierarchy of individual attribute levels.

library(cjdiag)
data(immig)

f <- Chosen_Immigrant ~ Gender + Education + LanguageSkills +
  CountryofOrigin + Job + JobExperience + JobPlans +
  ReasonforApplication + PriorEntry

Fit 

rf <- cj_fit(f, data = immig, method = "forest")
rf
#> Conjoint Random Forest 
#> ====================== 
#> 
#> Resolution: levels
#> Trees: 500
#> OOB Error: 40.3%
#> Observations: 2,000
#> Attributes: 9
#> Levels: 50
#> 
#> Top 10 levels by MDA:
#> 
#> # A tibble: 10 × 7
#>     rank attribute       level                      mda root_pct class_0 class_1
#>    <int> <chr>           <chr>                    <dbl>    <dbl>   <dbl>   <dbl>
#>  1     1 JobPlans        no plans to look for wo… 13.5      15.4  12.3      7.25
#>  2     2 JobPlans        contract with employer    8.18     11.2   3.70     6.98
#>  3     3 Education       no formal                 7.87      7.4   8.04     2.38
#>  4     4 PriorEntry      once w/o authorization    7.42     10.4   6.87     3.66
#>  5     5 LanguageSkills  fluent English            6.16      8.2   2.71     6.00
#>  6     6 PriorEntry      once as tourist           4.83      2.4   1.61     5.25
#>  7     7 Education       college degree            4.75      6.4   0.153    6.16
#>  8     8 LanguageSkills  used interpreter          4.66      5.6   4.91     1.37
#>  9     9 CountryofOrigin Iraq                      4.15      4.6   3.53     2.15
#> 10    10 Job             janitor                   3.87      3     2.09     3.36

Interpret the results table 

rf$results
#> # A tibble: 50 × 9
#>     rank attribute       level       mda   mdg root_pct class_0 class_1 var_name
#>    <int> <chr>           <chr>     <dbl> <dbl>    <dbl>   <dbl>   <dbl> <chr>   
#>  1     1 JobPlans        no plans… 13.5   28.2     15.4  12.3      7.25 JobPlan…
#>  2     2 JobPlans        contract…  8.18  23.0     11.2   3.70     6.98 JobPlan…
#>  3     3 Education       no formal  7.87  18.7      7.4   8.04     2.38 Educati…
#>  4     4 PriorEntry      once w/o…  7.42  22.9     10.4   6.87     3.66 PriorEn…
#>  5     5 LanguageSkills  fluent E…  6.16  22.3      8.2   2.71     6.00 Languag…
#>  6     6 PriorEntry      once as …  4.83  20.3      2.4   1.61     5.25 PriorEn…
#>  7     7 Education       college …  4.75  18.9      6.4   0.153    6.16 Educati…
#>  8     8 LanguageSkills  used int…  4.66  20.2      5.6   4.91     1.37 Languag…
#>  9     9 CountryofOrigin Iraq       4.15  17.1      4.6   3.53     2.15 Country…
#> 10    10 Job             janitor    3.87  18.0      3     2.09     3.36 Jobjani…
#> # ℹ 40 more rows

Key columns:

  • mda — Mean Decrease in Accuracy. How much the forest’s predictive accuracy drops when this level’s values are shuffled. Higher = more important. Levels with mda near zero are effectively ignored.
  • root_pct — % of trees in the forest where this level is the first split. The gatekeeper signal.
  • class_0 / class_1 — class-conditional importance. class_0 large = level mostly drives rejection (deal-breaker); class_1 large = level mostly drives selection (attractor).

Plot 

plot(rf, type = "rank", top_n = 20)