rand_forest_ranger.md
In parsnip: A Common API to Modeling and Analysis Functions

ranger regression intervals

Code
  rgr_se <- predict(extract_fit_engine(xy_fit), data = head(ames_x, 3), type = "se")$
    se
Condition
  Warning in `rInfJack()`:
  Sample size <=20, no calibration performed.
  Warning in `sqrt()`:
  NaNs produced

Code
  parsnip_int <- predict(xy_fit, new_data = head(ames_x, 3), type = "conf_int",
  std_error = TRUE, level = 0.93)
Condition
  Warning in `rInfJack()`:
  Sample size <=20, no calibration performed.
  Warning in `sqrt()`:
  NaNs produced

argument checks for data dimensions

Code
  f_fit <- spec %>% fit(body_mass_g ~ ., data = penguins)
Condition
  Warning:
  1000 columns were requested but there were 6 predictors in the data. 6 will be used.
  Warning:
  1000 samples were requested but there were 333 rows in the data. 333 will be used.

Code
  xy_fit <- spec %>% fit_xy(x = penguins[, -6], y = penguins$body_mass_g)
Condition
  Warning:
  1000 columns were requested but there were 6 predictors in the data. 6 will be used.
  Warning:
  1000 samples were requested but there were 333 rows in the data. 333 will be used.