A horizontal bar chart displaying permutation feature importance from machine learning models, showing the decrease in model score when each feature is randomly shuffled. Unlike model-specific feature importances, permutation importance is model-agnostic and measures how much the model's performance degrades when a feature's relationship with the target is broken. Error bars indicate variability across multiple shuffles, providing a confidence measure for each importance score.
#' anyplot.ai
#' bar-permutation-importance: Permutation Feature Importance Plot
#' Library: ggplot2 3.5.1 | R 4.4.1
#' Quality: 94/100 | Created: 2026-05-17
library(ggplot2)
library(dplyr)
library(tidyr)
library(scales)
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"
INK_MUTED <- if (THEME == "light") "#6B6A63" else "#A8A79F"
IMPRINT <- c("#009E73", "#C475FD", "#4467A3", "#BD8233",
"#AE3030", "#2ABCCD", "#954477")
# --- Data -------------------------------------------------------------------
# Simulate permutation importance from a machine learning model
# 15 features with realistic importance scores and variability
features <- c(
"Glucose", "Blood Pressure", "Skin Thickness", "Insulin", "BMI",
"Diabetes Pedigree", "Age", "Pregnancies", "Feature 9", "Feature 10",
"Feature 11", "Feature 12", "Feature 13", "Feature 14", "Feature 15"
)
importance_mean <- c(
0.085, 0.062, 0.041, 0.038, 0.127,
0.045, 0.093, 0.023, 0.018, 0.012,
0.009, 0.007, 0.005, 0.003, 0.001
)
importance_std <- c(
0.012, 0.008, 0.006, 0.007, 0.015,
0.006, 0.011, 0.004, 0.003, 0.002,
0.002, 0.001, 0.001, 0.001, 0.0005
)
df <- tibble::tibble(
feature = factor(features, levels = rev(features[order(importance_mean)])),
importance_mean = importance_mean,
importance_std = importance_std
) %>%
arrange(desc(importance_mean))
# --- Plot -------------------------------------------------------------------
anyplot_theme <- theme_minimal(base_size = 14) +
theme(
plot.background = element_rect(fill = PAGE_BG, color = PAGE_BG),
panel.background = element_rect(fill = PAGE_BG, color = NA),
panel.grid.major.x = element_line(color = INK_SOFT, linewidth = 0.3, linetype = "solid"),
panel.grid.major.y = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
axis.title = element_text(color = INK, size = 20),
axis.text.x = element_text(color = INK_SOFT, size = 16),
axis.text.y = element_text(color = INK_SOFT, size = 16),
axis.line.x = element_line(color = INK_SOFT, linewidth = 0.5),
axis.line.y = element_blank(),
axis.ticks.y = element_blank(),
plot.title = element_text(color = INK, size = 24, face = "plain"),
plot.margin = margin(t = 20, r = 20, b = 20, l = 20)
)
p <- ggplot(df, aes(x = importance_mean, y = reorder(feature, importance_mean))) +
# Vertical reference line at x=0
geom_vline(xintercept = 0, color = INK_SOFT, linewidth = 0.5, linetype = "solid") +
# Bars with color gradient based on importance
geom_col(
aes(fill = importance_mean),
width = 0.7,
color = NA
) +
# Error bars showing variability
geom_errorbarh(
aes(xmin = importance_mean - importance_std,
xmax = importance_mean + importance_std),
height = 0.3,
color = INK_SOFT,
linewidth = 0.5,
alpha = 0.7
) +
# Continuous color gradient for importance
scale_fill_gradient(
low = IMPRINT[1],
high = IMPRINT[2],
name = "Mean Importance",
labels = label_number(accuracy = 0.001)
) +
labs(
x = "Permutation Importance (decrease in model score)",
y = "Feature",
title = "bar-permutation-importance · ggplot2 · anyplot.ai"
) +
anyplot_theme +
theme(
legend.position = "right",
legend.background = element_rect(fill = ELEVATED_BG, color = INK_SOFT, linewidth = 0.5),
legend.text = element_text(color = INK_SOFT, size = 16),
legend.title = element_text(color = INK, size = 18),
legend.margin = margin(t = 10, r = 10, b = 10, l = 10)
)
# --- Save -------------------------------------------------------------------
ggsave(
filename = sprintf("plot-%s.png", THEME),
plot = p,
device = ragg::agg_png,
width = 16,
height = 9,
units = "in",
dpi = 300
)
Part of Permutation Feature Importance Plot on anyplot.ai.