diff --git a/slides/forests/rsrc/fig-nutshell-forest.R b/slides/forests/rsrc/fig-nutshell-forest.R
new file mode 100644
index 000000000..6820d3503
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+++ b/slides/forests/rsrc/fig-nutshell-forest.R
@@ -0,0 +1,277 @@
+# PREREQ -----------------------------------------------------------------------
+
+library(knitr)
+library(mlr3)
+library(mlr3learners)
+library(mlr3viz)
+library(ggplot2)
+library(ISLR)
+library(mlbench)
+library(kernlab)
+library(knitr)
+library(gridExtra)
+library(patchwork)
+
+
+credit_data <- Credit
+credit_data <- credit_data[, c("Rating", "Age","Income",  "Limit", "Balance")]
+
+
+options(digits = 3, 
+        width = 65, 
+        str = strOptions(strict.width = "cut", vec.len = 3))
+
+# DATA -------------------------------------------------------------------------
+
+
+
+set.seed(600000)
+task = TaskRegr$new("credit_task", 
+                    backend = credit_data, 
+                    target = "Balance"
+)
+task$select(c("Limit"))
+
+
+num_trees = c(1, 50, 50000)
+
+learners = lapply(num_trees, 
+                  function (i) {lrn("regr.ranger",
+                                    num.trees = i)}) 
+
+
+
+
+# PLOT 1 -----------------------------------------------------------------------
+
+plot1 <- plot_learner_prediction(learners[[1]], task) +
+  ggtitle(paste0(num_trees[1], " Tree(s) for Credit  Dataset")) +
+  guides(shape = FALSE, alpha = FALSE) + theme(axis.text=element_text(size=18),title=element_text(size = 22), axis.title=element_text(size=22), 
+                                               legend.key.size = unit(1.5, 'cm'), legend.position = "none") +
+  xlab("Limit") + ylab("Balance") 
+
+ggsave(plot = plot1, "figure/nutshell_forest_ensemblesize_1.pdf", width = 8, height = 5.5)
+
+
+# PLOT 2 -----------------------------------------------------------------------
+
+plot2 <- plot_learner_prediction(learners[[2]], task) +
+  ggtitle(paste0(num_trees[2], " Tree(s) for Credit  Dataset")) +
+  guides(shape = FALSE, alpha = FALSE) + theme(axis.text=element_text(size=18),title=element_text(size = 22), axis.title=element_text(size=22), 
+                                               legend.key.size = unit(1.5, 'cm'), legend.position = "none") +
+  xlab("Limit") + ylab("Balance") 
+
+ggsave(plot = plot2, "figure/nutshell_forest_ensemblesize_2.pdf", width = 8, height = 5.5)
+
+
+# PLOT 3 -----------------------------------------------------------------------
+
+plot3 <- plot_learner_prediction(learners[[3]], task) +
+  ggtitle(paste0(num_trees[3], " Tree(s) for Credit  Dataset")) +
+  guides(shape = FALSE, alpha = FALSE) + theme(axis.text=element_text(size=18),title=element_text(size = 22), axis.title=element_text(size=22), 
+                                               legend.key.size = unit(1.5, 'cm'), legend.position = "none") +
+  xlab("Limit") + ylab("Balance") 
+
+ggsave(plot = plot3, "figure/nutshell_forest_ensemblesize_3.pdf", width = 8, height = 5.5)
+
+
+# PLOT RF CLASSIFICATION
+
+options(digits = 3, 
+        width = 65, 
+        str = strOptions(strict.width = "cut", vec.len = 3))
+
+# DATA -------------------------------------------------------------------------
+set.seed(6)
+data = mlbench.twonorm(n = 100, d = 2)
+data = as.data.frame(data)
+X = as.matrix(data[, 1:2])
+y = data$classes
+colnames(data) <- c("x.1", "x.2", "Class")
+data$Class <- as.numeric(data$Class)
+data[data$Class == 2,3] <- 0
+data$Class <- as.factor(data$Class)
+
+task = TaskClassif$new("data_binary", data, target = "Class")
+task$select(c("x.1", "x.2"))
+
+# FUNCTIONS --------------------------------------------------------------------
+
+num_trees = c(1, 10, 200)
+
+learners_list = lapply(num_trees, 
+                       function (i) {lrn("classif.ranger",
+                                         num.trees = i, predict_type = "prob")}) 
+
+
+
+# PLOT 1 CLASSIFICTAION -TREE 1 --------------------------------------------------------------------
+set.seed(2)
+features = task$feature_names
+predict_sets = "train"
+task$data(cols = features)
+rr = mlr3::resample(task, learners_list[[1]]$clone(), mlr3::rsmp("holdout", ratio = 1), store_models = TRUE)
+grid = mlr3viz:::predict_grid(rr$learners, rr$task, grid_points = 100L, expand_range = 0)
+data = mlr3viz:::task_data(rr, predict_sets)
+data$.predict_set <- as.numeric(data$.predict_set)
+
+
+plot1 <- ggplot(grid, aes(
+  x = .data[[features[1L]]], 
+  y = .data[[features[2L]]])
+) + 
+  geom_raster(aes(fill = .data[["response"]], alpha = .data[[".prob.response"]])) + 
+  geom_point(
+    data = mlr3viz:::task_data(rr, predict_sets),size=3, 
+    aes(shape = .data[["Class"]], fill = .data[["Class"]],  color=.data[["Class"]]), color = "black") +
+  scale_fill_hue() + 
+  scale_shape_manual(values = c(21,24)) +
+  scale_alpha_continuous(name = "Probability") + 
+  labs(fill = "Class") + 
+  guides(alpha = FALSE, color = guide_legend(
+    override.aes=list(shape = 19))) +
+  ggtitle(paste0("Tree 1")) +
+  theme(axis.text=element_text(size=14),title=element_text(size = 16), axis.title=element_text(size=14), 
+        legend.key.size = unit(2.5, 'cm'), legend.title = element_text(size=14),
+        legend.text = element_text(size=14), legend.title.align=0.3, legend.position = "right")
+ggsave(plot = plot1, "figure/nutshell_classif_forest_ensemblesize_1.pdf", width = 8, height = 5.5)
+
+# PLOT 1 CLASSIFICATION - TREE 2 --------------------------------------------------------------------
+set.seed(30)
+features = task$feature_names
+predict_sets = "train"
+task$data(cols = features)
+rr = mlr3::resample(task, learners_list[[1]]$clone(), mlr3::rsmp("holdout", ratio = 1), store_models = TRUE)
+grid = mlr3viz:::predict_grid(rr$learners, rr$task, grid_points = 100L, expand_range = 0)
+data = mlr3viz:::task_data(rr, predict_sets)
+data$.predict_set <- as.numeric(data$.predict_set)
+
+plot1_2 <- ggplot(grid, aes(
+  x = .data[[features[1L]]], 
+  y = .data[[features[2L]]])
+) + 
+  geom_raster(aes(fill = .data[["response"]], alpha = .data[[".prob.response"]])) + 
+  geom_point(
+    data = mlr3viz:::task_data(rr, predict_sets),size=3, 
+    aes(shape = .data[["Class"]], fill = .data[["Class"]],  color=.data[["Class"]]), color = "black") +
+  scale_fill_hue() + 
+  scale_shape_manual(values = c(21,24)) +
+  scale_alpha_continuous(name = "Probability") + 
+  labs(fill = "Class") + 
+  guides(alpha = FALSE, color = guide_legend(
+    override.aes=list(shape = 19))) +
+  ggtitle(paste0("Tree 2")) +
+  theme(axis.text=element_text(size=14),title=element_text(size = 16), axis.title=element_text(size=14), 
+        legend.key.size = unit(2.5, 'cm'), legend.title = element_text(size=14),
+        legend.text = element_text(size=14), legend.title.align=0.3, legend.position = "right")
+ggsave(plot = plot1_2, "figure/nutshell_classif_forest_ensemblesize_1_TREE_2.pdf", width = 8, height = 5.5)
+
+# PLOT 2 CLASSIFICATION
+set.seed(2)
+features = task$feature_names
+predict_sets = "train"
+task$data(cols = features)
+rr = mlr3::resample(task, learners_list[[2]]$clone(), mlr3::rsmp("holdout", ratio = 1), store_models = TRUE)
+grid = mlr3viz:::predict_grid(rr$learners, rr$task, grid_points = 100L, expand_range = 0)
+data = mlr3viz:::task_data(rr, predict_sets)
+data$.predict_set <- as.numeric(data$.predict_set)
+
+
+plot2 <- ggplot(grid, aes(
+  x = .data[[features[1L]]], 
+  y = .data[[features[2L]]])
+) + 
+  geom_raster(aes(fill = .data[["response"]], alpha = .data[[".prob.response"]])) + 
+  geom_point(
+    data = mlr3viz:::task_data(rr, predict_sets),size=3, 
+    aes(shape = .data[["Class"]], fill = .data[["Class"]],  color=.data[["Class"]]), color = "black") +
+  scale_fill_hue() + 
+  scale_shape_manual(values = c(21,24)) +
+  scale_alpha_continuous(name = "Probability") + 
+  labs(fill = "Class") + 
+  guides(alpha = FALSE, color = guide_legend(
+    override.aes=list(shape = 19))) +
+  ggtitle(paste0(num_trees[2], " Random Trees for Classification Task")) +
+  theme(axis.text=element_text(size=14),title=element_text(size = 16), axis.title=element_text(size=14), 
+        legend.key.size = unit(1.5, 'cm'), legend.title = element_text(size=14),
+        legend.text = element_text(size=14), legend.title.align=0.3, legend.position = "right")
+
+ggsave(plot = plot2, "figure/nutshell_classif_forest_ensemblesize_2.pdf", width = 8, height = 5.5)
+
+# PLOT 3 CLASSIFICATION
+set.seed(2)
+features = task$feature_names
+predict_sets = "train"
+task$data(cols = features)
+rr = mlr3::resample(task, learners_list[[3]]$clone(), mlr3::rsmp("holdout", ratio = 1), store_models = TRUE)
+grid = mlr3viz:::predict_grid(rr$learners, rr$task, grid_points = 100L, expand_range = 0)
+data = mlr3viz:::task_data(rr, predict_sets)
+data$.predict_set <- as.numeric(data$.predict_set)
+
+
+
+plot3 <- ggplot(grid, aes(
+  x = .data[[features[1L]]], 
+  y = .data[[features[2L]]])
+) + 
+  geom_raster(aes(fill = .data[["response"]], alpha = .data[[".prob.response"]])) + 
+  geom_point(
+    data = mlr3viz:::task_data(rr, predict_sets),size=3, 
+    aes(shape = .data[["Class"]], fill = .data[["Class"]],  color=.data[["Class"]]), color = "black") +
+  scale_fill_hue() + 
+  scale_shape_manual(values = c(21,24)) +
+  scale_alpha_continuous(name = "Probability") + 
+  labs(fill = "Class") + 
+  guides(alpha = FALSE, color = guide_legend(
+    override.aes=list(shape = 19))) +
+  ggtitle(paste0("RF 1")) +
+  theme(axis.text=element_text(size=14),title=element_text(size = 16), axis.title=element_text(size=14), 
+        legend.key.size = unit(2.5, 'cm'), legend.title = element_text(size=14),
+        legend.text = element_text(size=14), legend.title.align=0.3, legend.position = "right")
+
+ggsave(plot = plot3, "figure/nutshell_classif_forest_ensemblesize_3.pdf", width = 8, height = 5.5)
+
+# PLOT 3 CLASSIFICATION - TREE 2 --------------------------------------------------------------------
+
+set.seed(30)
+features = task$feature_names
+predict_sets = "train"
+task$data(cols = features)
+rr = mlr3::resample(task, learners_list[[3]]$clone(), mlr3::rsmp("holdout", ratio = 1), store_models = TRUE)
+grid = mlr3viz:::predict_grid(rr$learners, rr$task, grid_points = 100L, expand_range = 0)
+data = mlr3viz:::task_data(rr, predict_sets)
+data$.predict_set <- as.numeric(data$.predict_set)
+
+
+
+plot3_2 <- ggplot(grid, aes(
+  x = .data[[features[1L]]], 
+  y = .data[[features[2L]]])
+) + 
+  geom_raster(aes(fill = .data[["response"]], alpha = .data[[".prob.response"]])) + 
+  geom_point(
+    data = mlr3viz:::task_data(rr, predict_sets),size=3, 
+    aes(shape = .data[["Class"]], fill = .data[["Class"]],  color=.data[["Class"]]), color = "black") +
+  scale_fill_hue() + 
+  scale_shape_manual(values = c(21,24)) +
+  scale_alpha_continuous(name = "Probability") + 
+  labs(fill = "Class") + 
+  guides(alpha = FALSE, color = guide_legend(
+    override.aes=list(shape = 19))) +
+  ggtitle(paste0("RF 2")) +
+  theme(axis.text=element_text(size=14),title=element_text(size = 16), axis.title=element_text(size=14), 
+        legend.key.size = unit(2.5, 'cm'), legend.title = element_text(size=14),
+        legend.text = element_text(size=14), legend.title.align=0.3, legend.position = "right")
+
+ggsave(plot = plot3_2, "figure/nutshell_classif_forest_ensemblesize_3_TREE_3.pdf", width = 8, height = 5.5)
+
+
+combined_plot_1 <-  plot1 + plot1_2 +  plot3 + plot3_2 + plot_layout(guides = "collect") 
+combined_plot_2 <-  plot1_2 + plot3_2 + plot_layout(guides = "collect") 
+
+ggsave(plot = combined_plot_1, "figure/nutshell_classif_combined_1.pdf", width = 11, height = 10)
+ggsave(plot = combined_plot_2, "figure/nutshell_classif_combined_2.pdf", width = 10, height = 4)
+
+
+# PLOT K-NN BAGGED
+