Random Forests

Random forests are an ensemble learning method for classification (and regression) that operate by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes output by individual trees – Wikipedia

Why a forest instead of a tree?

• Trees suffer from very high variance

• Two datasets will provide extremely different trees

• To reduce variability, fit many trees and aggregate the predictions.

• No longer reporting the tree structure, now reporting the predicted values (with hopes that predictions from one dataset will be similar to predictions from another dataset).

Bagging

Bagging = Bootstrap Aggregating.

• multiple models aggregate together will produce a smoother model fit and better balance between
  • bias in the fit
  • variance in the fit.

Bagging can be applied to any classifier to reduce variability.

Recall that the variance of the sample mean is variance of data divided by $n$. So we’ve seen the idea that averaging a set of outcomes gives reduced variability.

Bagging Models

Image credit: https://www.pluralsight.com/guides/ensemble-methods:-bagging-versus-boosting

Bagging Trees

Image credit: https://www.analyticsvidhya.com/blog/2021/05/bagging-25-questions-to-test-your-skills-on-random-forest-algorithm/

Observations not in a given tree

P(observation $i$ is not in the bootstrap sample) = $(1-\frac{1}{n}{)}^n$

$$\underset{n\to \mathrm{\infty }}{lim}(1-\frac{1}{n}{)}^n=\frac{1}{e}\approx \frac{1}{3}$$

OOB Error

Out of Bag

with bagging, there is no need for cross-validation or a separate test set to get an unbiased estimate of the test set error. It is estimated internally for free!!!

• Each tree is constructed using a different bootstrap sample

• Put all of the cases left out in the construction of the $b^{th}$ tree down the $b^{th}$ tree to get a classification.

• At the end of the run,

  • take $j$ to be the class that got most of the votes every time case $i$ was oob.
  • The proportion of times that $j$ is not equal to the true class averaged over all cases is the oob error.

Bagging example w defaults

penguin_bag_recipe <-
  recipe(body_mass_g ~ . ,
         data = penguin_train) |>
  step_unknown(sex, new_level = "unknown") |>
  step_mutate(year = as.factor(year)) |>
  step_naomit(all_predictors(), body_mass_g)

penguin_bag_recipe

── Recipe ──────────────────────────────────────────────────────────────────────

── Inputs 

Number of variables by role

outcome:   1
predictor: 7

── Operations 

• Unknown factor level assignment for: sex

• Variable mutation for: as.factor(year)

• Removing rows with NA values in: all_predictors() body_mass_g

num_trees <- 500
penguin_bag <- rand_forest(mtry = 7,
                           trees = num_trees) |>
  set_engine("randomForest", oob.error = TRUE) |>
  set_mode("regression")

penguin_bag

Random Forest Model Specification (regression)

Main Arguments:
  mtry = 7
  trees = num_trees

Engine-Specific Arguments:
  oob.error = TRUE

Computational engine: randomForest 

penguin_bag_wflow <- workflow() |>
  add_model(penguin_bag) |>
  add_recipe(penguin_bag_recipe)

penguin_bag_wflow

══ Workflow ════════════════════════════════════════════════════════════════════
Preprocessor: Recipe
Model: rand_forest()

── Preprocessor ────────────────────────────────────────────────────────────────
3 Recipe Steps

• step_unknown()
• step_mutate()
• step_naomit()

── Model ───────────────────────────────────────────────────────────────────────
Random Forest Model Specification (regression)

Main Arguments:
  mtry = 7
  trees = num_trees

Engine-Specific Arguments:
  oob.error = TRUE

Computational engine: randomForest 

penguin_bag_fit <- 
  penguin_bag_wflow |>
  fit(data = penguin_train)

penguin_bag_fit 

══ Workflow [trained] ══════════════════════════════════════════════════════════
Preprocessor: Recipe
Model: rand_forest()

── Preprocessor ────────────────────────────────────────────────────────────────
3 Recipe Steps

• step_unknown()
• step_mutate()
• step_naomit()

── Model ───────────────────────────────────────────────────────────────────────

Call:
 randomForest(x = maybe_data_frame(x), y = y, ntree = ~num_trees,      mtry = min_cols(~7, x), oob.error = ~TRUE) 
               Type of random forest: regression
                     Number of trees: 500
No. of variables tried at each split: 7

          Mean of squared residuals: 92247.19
                    % Var explained: 85.06

Fit again

══ Workflow [trained] ══════════════════════════════════════════════════════════
Preprocessor: Recipe
Model: rand_forest()

── Preprocessor ────────────────────────────────────────────────────────────────
3 Recipe Steps

• step_unknown()
• step_mutate()
• step_naomit()

── Model ───────────────────────────────────────────────────────────────────────

Call:
 randomForest(x = maybe_data_frame(x), y = y, ntree = ~num_trees,      mtry = min_cols(~7, x), oob.error = ~TRUE) 
               Type of random forest: regression
                     Number of trees: 500
No. of variables tried at each split: 7

          Mean of squared residuals: 92405.61
                    % Var explained: 85.03

Plotting the OOB (not tidy)

rsq <- penguin_bag_fit |>
  extract_fit_parsnip() |>
  pluck("fit") |>
  pluck("rsq")


data.frame(
  trees = 1:num_trees, 
  rsq = rsq) |>
  ggplot() + 
  geom_line(
    aes(x = trees, y = rsq)) +
  ylab("") + 
  xlab("Number of Trees") + 
  ggtitle("OOB R-squared")

MSE <- penguin_bag_fit |>
  extract_fit_parsnip() |>
  pluck("fit") |>
  pluck("mse")

data.frame(
  trees = 1:num_trees, 
  MSE = MSE) |>
  ggplot() + 
  geom_line(
    aes(x = trees, y = MSE)) +
  ylab("") + 
  xlab("Number of Trees") + 
  ggtitle("OOB MSE")

Random Forests

• Many trees

  • bootstrap the data before building each tree
  • for best split look only at a random $m$ variables (different $m$ variables per split)

• Tuning: look at OOB predictions – either average or majority vote

• Testing: test data will be (average or majority vote) prediction for all the trees.

RF visualized

Image credit: https://medium.com/data-science/random-forest-explained-a-visual-guide-with-code-examples-9f736a6e1b3c

Overall misclassification rate

The error rate (for classification forest) is: $$\frac{\mathrm{\#}\text{ misclassificiations}}{n}$$

• The prediction of an observation come in two steps. First, an observation is run down a tree until it gets to a terminal node. The majority class in that terminal node (from the training observations) provides the class for the prediction from that tree. The observation goes down all the relevant trees, each providing a class. Second, the final prediction of the observation is given by the majority class across the trees.

• Each observation will get one prediction. If the predicted class is different from the observed class, the point is considered to be misclassified.

• Keep in mind that the trees and forests that will be used are different depending on whether the error rate is calculated under OOB, CV, training, or testing scenarios.

Variable Importance

importance = decrease in node impurity resulting from splits over that variable, averaged over all trees.

• For each tree, the prediction error on the out-of-bag portion of the data is recorded (error rate for classification, MSE for regression).

• Keep the set of trees, and permute the $j^{th}$ variable (of the observation being predicted) and recalculate the oob prediction error.

• The difference between the two oob errors are averaged over all trees (for the $j^{th}$ variable) to give the importance for the $j^{th}$ variable.

RF technical details

• unfortunately, tidymodels currently does not have an OOB option for model tuning (e.g., to find mtry)

• unlike num_trees, mtry is not calculated “as you go along”

• cross validation will be used to find the optimal value of mtry

RF example w CV tuning

penguin_rf_recipe <-
  recipe(body_mass_g ~ . ,
         data = penguin_train) |>
  step_unknown(sex, new_level = "unknown") |>
  step_mutate(year = as.factor(year)) 

penguin_rf_recipe

── Recipe ──────────────────────────────────────────────────────────────────────

── Inputs 

Number of variables by role

outcome:   1
predictor: 7

── Operations 

• Unknown factor level assignment for: sex

• Variable mutation for: as.factor(year)

penguin_rf <- rand_forest(mtry = tune(),
                           trees = tune()) |>
  set_engine("ranger", importance = "permutation") |>
  set_mode("regression")

penguin_rf

Random Forest Model Specification (regression)

Main Arguments:
  mtry = tune()
  trees = tune()

Engine-Specific Arguments:
  importance = permutation

Computational engine: ranger 

penguin_rf_wflow <- workflow() |>
  add_model(penguin_rf) |>
  add_recipe(penguin_rf_recipe)

penguin_rf_wflow

══ Workflow ════════════════════════════════════════════════════════════════════
Preprocessor: Recipe
Model: rand_forest()

── Preprocessor ────────────────────────────────────────────────────────────────
2 Recipe Steps

• step_unknown()
• step_mutate()

── Model ───────────────────────────────────────────────────────────────────────
Random Forest Model Specification (regression)

Main Arguments:
  mtry = tune()
  trees = tune()

Engine-Specific Arguments:
  importance = permutation

Computational engine: ranger 

set.seed(234)
penguin_folds <- vfold_cv(penguin_train,
                          v = 4)

# grid_regular allows for ranges
penguin_grid <- grid_regular(mtry(range = c(2,7)),
                             trees(range = c(1,500)), levels = 5)

penguin_grid

# A tibble: 25 × 2
    mtry trees
   <int> <int>
 1     2     1
 2     3     1
 3     4     1
 4     5     1
 5     7     1
 6     2   125
 7     3   125
 8     4   125
 9     5   125
10     7   125
# ℹ 15 more rows

penguin_rf_tune <- 
  penguin_rf_wflow |>
  tune_grid(resamples = penguin_folds,
            grid = penguin_grid)

penguin_rf_tune 

# Tuning results
# 4-fold cross-validation 
# A tibble: 4 × 4
  splits           id    .metrics          .notes          
  <list>           <chr> <list>            <list>          
1 <split [186/63]> Fold1 <tibble [50 × 6]> <tibble [0 × 3]>
2 <split [187/62]> Fold2 <tibble [50 × 6]> <tibble [0 × 3]>
3 <split [187/62]> Fold3 <tibble [50 × 6]> <tibble [0 × 3]>
4 <split [187/62]> Fold4 <tibble [50 × 6]> <tibble [0 × 3]>

RF model output

penguin_rf_tune |>
  collect_metrics() |>
  filter(.metric == "rmse") |>
  mutate(mtry = as.factor(mtry)) |> 
  ggplot() + 
  geom_line(aes(x = trees, y = mean, color = mtry)) + 
  labs(title = "RMSE: root mean squared error", y = "")

RF Final model

penguin_rf_best <- finalize_model(
  penguin_rf,
  select_best(penguin_rf_tune, metric = "rmse"))

penguin_rf_best

Random Forest Model Specification (regression)

Main Arguments:
  mtry = 2
  trees = 375

Engine-Specific Arguments:
  importance = permutation

Computational engine: ranger 

penguin_rf_final <-
  workflow() |>
  add_model(penguin_rf_best) |>
  add_recipe(penguin_rf_recipe) |>
  fit(data = penguin_train)

RF Final model

penguin_rf_final

══ Workflow [trained] ══════════════════════════════════════════════════════════
Preprocessor: Recipe
Model: rand_forest()

── Preprocessor ────────────────────────────────────────────────────────────────
2 Recipe Steps

• step_unknown()
• step_mutate()

── Model ───────────────────────────────────────────────────────────────────────
Ranger result

Call:
 ranger::ranger(x = maybe_data_frame(x), y = y, mtry = min_cols(~2L,      x), num.trees = ~375L, importance = ~"permutation", num.threads = 1,      verbose = FALSE, seed = sample.int(10^5, 1)) 

Type:                             Regression 
Number of trees:                  375 
Sample size:                      249 
Number of independent variables:  7 
Mtry:                             2 
Target node size:                 5 
Variable importance mode:         permutation 
Splitrule:                        variance 
OOB prediction error (MSE):       84149.09 
R squared (OOB):                  0.8634591 

Test predictions

penguin_rf_final |>
  predict(new_data = penguin_test) |>
  cbind(penguin_test) |>
  ggplot() +
  geom_point(aes(x = body_mass_g, y = .pred)) + 
  geom_abline(intercept = 0, slope = 1)

Model choices

Bias-Variance Tradeoff

Test and training error as a function of model complexity. Note that the error goes down monotonically only for the training data. Be careful not to overfit!! image credit: ISLR

Reflecting on Model Building

Image credit: https://www.tmwr.org/

Reflecting on Model Building

Image credit: https://www.tmwr.org/

Reflecting on Model Building

Image credit: https://www.tmwr.org/