In bcjaeger/aorsf: Accelerated Oblique Random Forests

You can compute partial dependence and individual conditional expectations in three ways:

• using in-bag predictions for the training data. In-bag partial dependence indicates relationships that the model has learned during training. This is helpful if your goal is to interpret the model.

• using out-of-bag predictions for the training data. Out-of-bag partial dependence indicates relationships that the model has learned during training but using the out-of-bag data simulates application of the model to new data. This is helpful if you want to test your model's reliability or fairness in new data but you don't have access to a large testing set.

• using predictions for a new set of data. New data partial dependence shows how the model predicts outcomes for observations it has not seen. This is helpful if you want to test your model's reliability or fairness.

Classification

Begin by fitting an oblique classification random forest:

Compute partial dependence using out-of-bag data for flipper_length_mm = c(190, 210).

pred_spec <- list(flipper_length_mm = c(190, 210))

pd_oob <- orsf_pd_oob(fit_clsf, pred_spec = pred_spec)

pd_oob

Note that predicted probabilities are returned for each class and probabilities in the mean column sum to 1 if you take the sum over each class at a specific value of the pred_spec variables. For example,

sum(pd_oob[flipper_length_mm == 190, mean])

But this isn't the case for the median predicted probability!

sum(pd_oob[flipper_length_mm == 190, medn])

Regression

Begin by fitting an oblique regression random forest:

Compute partial dependence using new data for flipper_length_mm = c(190, 210).

pred_spec <- list(flipper_length_mm = c(190, 210))

pd_new <- orsf_pd_new(fit_regr, 
                      pred_spec = pred_spec,
                      new_data = penguins_orsf_test)

pd_new

You can also let pred_spec_auto pick reasonable values like so:

pred_spec = pred_spec_auto(species, island, body_mass_g)

pd_new <- orsf_pd_new(fit_regr, 
                      pred_spec = pred_spec,
                      new_data = penguins_orsf_test)

pd_new

By default, all combinations of all variables are used. However, you can also look at the variables one by one, separately, like so:

pd_new <- orsf_pd_new(fit_regr, 
                      expand_grid = FALSE,
                      pred_spec = pred_spec,
                      new_data = penguins_orsf_test)

pd_new

And you can also bypass all the bells and whistles by using your own data.frame for a pred_spec. (Just make sure you request values that exist in the training data.)

custom_pred_spec <- data.frame(species = 'Adelie', 
                               island = 'Biscoe')

pd_new <- orsf_pd_new(fit_regr, 
                      pred_spec = custom_pred_spec,
                      new_data = penguins_orsf_test)

pd_new

Survival

Begin by fitting an oblique survival random forest:

Compute partial dependence using in-bag data for bili = c(1,2,3,4,5):

pd_train <- orsf_pd_inb(fit_surv, pred_spec = list(bili = 1:5))
pd_train

If you don't have specific values of a variable in mind, let pred_spec_auto pick for you:

pd_train <- orsf_pd_inb(fit_surv, pred_spec_auto(bili))
pd_train

Specify pred_horizon to get partial dependence at each value:

pd_train <- orsf_pd_inb(fit_surv, pred_spec_auto(bili),
                        pred_horizon = seq(500, 3000, by = 500))
pd_train

bcjaeger/aorsf documentation built on April 3, 2025, 4:16 p.m.