important

The important package has a succinct interface for obtaining estimates of predictor importance with tidymodels objects. A few of the main features:

• Any performance metrics from the yardstick package can be used.
• Importance can be calculated for either the original columns or at the level of any derived model terms created during feature engineering.
• The computations that loop across permutation iterations and predictors columns are easily parallelized.
• The results are returned in a tidy format.

Installation

You can install the development version of important from GitHub with:

install.packages("devtools")
# or
pak::pak("tidymodels/important")

Do we really need another package that computes variable importances?

The main reason for making important is censored regression models. tidymodels released tools for fitting and qualifying models that have censored outcomes. This included some dynamic performance metrics that were evaluated at different time points. This was a substantial change for us, and it would have been even more challenging to add to other packages.

Example

Let’s look at an analysis that models food delivery times. The outcome is the time between an order being placed and the delivery (all data are complete - there is no censoring). We model this in terms of the order day/time, the distance to the restaurant, and which items are contained in the order. Exploratory data analysis shows several nonlinear trends in the data and some interactions between these trends.

We’ll load the tidymodels and important packages to get started.

The data are split into training, validation, and testing sets.

data(deliveries, package = "modeldata")

set.seed(991)
delivery_split <- initial_validation_split(deliveries, prop = c(0.6, 0.2), strata = time_to_delivery)
delivery_train <- training(delivery_split)

The model uses a recipe with spline terms for the hour and distances. The nonlinear trend over the time of order changes on the day, so we added interactions between these two sets of terms. Finally, a simple linear regression model is used for estimation:

delivery_rec <- 
  recipe(time_to_delivery ~ ., data = delivery_train) |> 
  step_dummy(all_factor_predictors()) |> 
  step_zv(all_predictors()) |> 
  step_spline_natural(hour, distance, deg_free = 10) |> 
  step_interact(~ starts_with("hour_"):starts_with("day_"))

lm_wflow <- workflow(delivery_rec, linear_reg())
lm_fit <- fit(lm_wflow, delivery_train)

First, let’s capture the effect of the individual model terms. These terms are from the derived features in the models, such as dummy variables, spline terms, interaction columns, etc.

set.seed(382)
lm_deriv_imp <- 
  importance_perm(
    lm_fit,
    data = delivery_train,
    metrics = metric_set(mae, rsq),
    times = 50,
    type = "derived"
  )
lm_deriv_imp
#> # A tibble: 226 × 6
#>    .metric predictor             n  mean std_err importance
#>    <chr>   <chr>             <int> <dbl>   <dbl>      <dbl>
#>  1 rsq     distance_10          50 0.528 0.00655       80.5
#>  2 mae     day_Sat              50 1.09  0.0150        72.2
#>  3 mae     distance_10          50 2.20  0.0323        68.2
#>  4 mae     day_Fri              50 0.877 0.0140        62.7
#>  5 mae     day_Thu              50 0.638 0.0130        49.0
#>  6 mae     distance_09          50 0.740 0.0156        47.3
#>  7 mae     hour_08              50 0.520 0.0136        38.3
#>  8 rsq     day_Sat              50 0.118 0.00327       36.2
#>  9 rsq     hour_06_x_day_Sat    50 0.146 0.00410       35.7
#> 10 mae     hour_08_x_day_Sat    50 0.604 0.0173        34.9
#> # ℹ 216 more rows

Using mean absolute error as the metric of interest, the top 5 features are:

lm_deriv_imp |> 
    filter(.metric == "mae") |> 
    slice_max(importance, n = 5)
#> # A tibble: 5 × 6
#>   .metric predictor       n  mean std_err importance
#>   <chr>   <chr>       <int> <dbl>   <dbl>      <dbl>
#> 1 mae     day_Sat        50 1.09   0.0150       72.2
#> 2 mae     distance_10    50 2.20   0.0323       68.2
#> 3 mae     day_Fri        50 0.877  0.0140       62.7
#> 4 mae     day_Thu        50 0.638  0.0130       49.0
#> 5 mae     distance_09    50 0.740  0.0156       47.3

Two notes:

• The importance scores are the ratio of the mean change in performance and the associated standard error. The mean value is always increasing with importance, no matter which direction is preferred for the specific metric(s).

• We can run these in parallel by loading the future package and specifying a parallel backend using the plan() function.

There is a plot method that can help visualize the results:

autoplot(lm_deriv_imp, top = 50)

Since there are spline terms and interactions for the hour column, we might not care about the importance of a term such as hour_06 (the sixth spline feature). In aggregate, we might want to know the effect of the original predictor columns. The type option is used for this purpose:

set.seed(382)
lm_orig_imp <- 
    importance_perm(
        lm_fit,
        data = delivery_train,
        metrics = metric_set(mae, rsq),
        times = 50,
        type = "original"
    )

# Top five: 
lm_orig_imp |> 
    filter(.metric == "mae") |> 
    slice_max(importance, n = 5)
#> # A tibble: 5 × 6
#>   .metric predictor     n   mean std_err importance
#>   <chr>   <chr>     <int>  <dbl>   <dbl>      <dbl>
#> 1 mae     hour         50 4.10    0.0320     128.  
#> 2 mae     day          50 1.91    0.0214      89.1 
#> 3 mae     distance     50 1.46    0.0222      66.0 
#> 4 mae     item_24      50 0.0489  0.0119       4.11
#> 5 mae     item_03      50 0.0337  0.0114       2.95

autoplot(lm_orig_imp)

Code of Conduct

Please note that the important project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.