A decision boundary visualization showing how a classifier partitions 2D feature space into predicted class regions. Colored regions indicate the predicted class at each point in the feature space, with training data points overlaid to show how well the classifier separates different classes. This visualization is essential for understanding classifier behavior, identifying decision boundaries, and evaluating classification accuracy in machine learning.
#' anyplot.ai
#' contour-decision-boundary: Decision Boundary Classifier Visualization
#' Library: ggplot2 3.5.1 | R 4.4.1
#' Quality: 88/100 | Created: 2026-09-04
library(ggplot2)
library(class)
library(ragg)
set.seed(42)
# --- Theme tokens ------------------------------------------------------------
THEME <- Sys.getenv("ANYPLOT_THEME", "light")
PAGE_BG <- if (THEME == "light") "#FAF8F1" else "#1A1A17"
ELEVATED_BG <- if (THEME == "light") "#FFFDF6" else "#242420"
INK <- if (THEME == "light") "#1A1A17" else "#F0EFE8"
INK_SOFT <- if (THEME == "light") "#4A4A44" else "#B8B7B0"
# Imprint palette — first 3 categorical slots (one per species)
IMPRINT_PALETTE <- c("#009E73", "#C475FD", "#4467A3")
# --- Data ----------------------------------------------------------------
# Iris petal measurements: a real, well-separated 3-class dataset for a
# k-nearest-neighbors decision surface.
train_df <- tibble::tibble(
petal_length = iris$Petal.Length,
petal_width = iris$Petal.Width,
species = iris$Species
)
# Dense mesh grid spanning the feature space, for the decision surface
grid_res <- 150
x_range <- range(train_df$petal_length)
y_range <- range(train_df$petal_width)
x_pad <- diff(x_range) * 0.08
y_pad <- diff(y_range) * 0.08
grid_df <- expand.grid(
petal_length = seq(x_range[1] - x_pad, x_range[2] + x_pad, length.out = grid_res),
petal_width = seq(y_range[1] - y_pad, y_range[2] + y_pad, length.out = grid_res)
)
k_neighbors <- 9
grid_df$predicted <- class::knn(
train = train_df[, c("petal_length", "petal_width")],
test = grid_df[, c("petal_length", "petal_width")],
cl = train_df$species,
k = k_neighbors
)
# Leave-one-out predictions on the training points flag misclassifications
train_df$predicted <- class::knn.cv(
train = train_df[, c("petal_length", "petal_width")],
cl = train_df$species,
k = k_neighbors
)
train_df$status <- ifelse(
train_df$predicted == train_df$species,
"Correctly classified",
"Misclassified"
)
# --- Plot ----------------------------------------------------------------
title_text <- "Iris Species by Petal Size · contour-decision-boundary · r · ggplot2 · anyplot.ai"
p <- ggplot() +
geom_tile(
data = grid_df,
aes(x = petal_length, y = petal_width, fill = predicted),
alpha = 0.32
) +
geom_point(
data = train_df,
aes(x = petal_length, y = petal_width, color = species, shape = status),
size = 2.5, stroke = 0.9
) +
scale_fill_manual(values = IMPRINT_PALETTE, guide = "none") +
scale_color_manual(values = IMPRINT_PALETTE, name = "Species") +
scale_shape_manual(
values = c("Correctly classified" = 16, "Misclassified" = 4),
name = "Prediction"
) +
labs(
title = title_text,
x = "Petal Length (cm)",
y = "Petal Width (cm)"
) +
theme_minimal(base_size = 8) +
theme(
plot.background = element_rect(fill = PAGE_BG, color = PAGE_BG),
panel.background = element_rect(fill = PAGE_BG, color = NA),
panel.grid = element_blank(),
axis.title = element_text(color = INK, size = 10),
axis.text = element_text(color = INK_SOFT, size = 8),
axis.line = element_line(color = INK_SOFT),
plot.title = element_text(color = INK, size = 10, face = "bold"),
legend.background = element_rect(fill = ELEVATED_BG, color = INK_SOFT),
legend.text = element_text(color = INK_SOFT, size = 8),
legend.title = element_text(color = INK, size = 10),
legend.key = element_rect(fill = PAGE_BG, color = NA)
)
# --- Save ----------------------------------------------------------------
ggsave(
filename = sprintf("plot-%s.png", THEME),
plot = p,
device = ragg::agg_png,
width = 8,
height = 4.5,
units = "in",
dpi = 400
)
Runnable source as JSON, for any HTTP client: https://api.anyplot.ai/specs/contour-decision-boundary/ggplot2/code. Any spec id and library id listed in llms-full.txt fit the same URL shape; every URL below is complete and callable.
{
"spec_id": "contour-decision-boundary",
"language": "r",
"library": "ggplot2",
"page": "https://anyplot.ai/contour-decision-boundary/r/ggplot2",
"hub": "https://anyplot.ai/contour-decision-boundary",
"code_json": "https://api.anyplot.ai/specs/contour-decision-boundary/ggplot2/code",
"spec_json": "https://api.anyplot.ai/specs/contour-decision-boundary",
"render_light_png": "https://storage.googleapis.com/anyplot-images/plots/contour-decision-boundary/r/ggplot2/plot-light.png",
"render_dark_png": "https://storage.googleapis.com/anyplot-images/plots/contour-decision-boundary/r/ggplot2/plot-dark.png",
"quality_score": 88.0,
"license": "MIT",
"guide": "https://anyplot.ai/llms.txt"
}Part of Decision Boundary Classifier Visualization on anyplot.ai.