A lift curve visualizes how much better a predictive model performs compared to random selection, showing the cumulative lift ratio as you target increasing percentages of the population. It answers the question: "If I target the top X% of predictions, how many times more responders will I capture than random targeting?" This plot is essential for evaluating and comparing classification models in scenarios where targeting efficiency matters.
#' anyplot.ai
#' lift-curve: Model Lift Chart
#' Library: ggplot2 3.5.1 | R 4.4.1
#' Quality: 92/100 | Created: 2026-09-05
library(ggplot2)
library(dplyr)
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"
GRID_LINE <- if (THEME == "light") "#D8D7D0" else "#3A3A36"
IMPRINT_PALETTE <- c("#009E73", "#C475FD", "#4467A3", "#BD8233",
"#AE3030", "#2ABCCD", "#954477", "#99B314")
# --- Data: fraud detection model scores -------------------------------------
n_transactions <- 4000
fraud_rate <- 0.06
is_fraud <- rbinom(n_transactions, 1, fraud_rate)
fraud_score <- rbeta(n_transactions, 6, 2)
legit_score <- rbeta(n_transactions, 2, 6)
model_score <- ifelse(is_fraud == 1, fraud_score, legit_score)
transactions <- tibble::tibble(is_fraud = is_fraud, model_score = model_score) %>%
arrange(desc(model_score)) %>%
mutate(
rank = row_number(),
pct_targeted = rank / n() * 100,
cum_capture_rate = cumsum(is_fraud) / rank,
lift = cum_capture_rate / mean(is_fraud)
)
decile_marks <- transactions %>%
filter(rank %in% round(n() * seq(0.1, 1.0, by = 0.1)))
callouts <- decile_marks %>%
filter(rank %in% round(n_transactions * c(0.1, 0.5))) %>%
mutate(label = sprintf("%.1fx @ %d%%", lift, round(pct_targeted)))
# --- Plot ---------------------------------------------------------------
p <- ggplot(transactions, aes(x = pct_targeted, y = lift)) +
geom_hline(aes(yintercept = 1, color = "Random baseline"),
linetype = "dashed", linewidth = 0.8) +
geom_line(aes(color = "Model"), linewidth = 1.2) +
geom_point(data = decile_marks, color = IMPRINT_PALETTE[1], size = 3) +
annotate(
"text",
x = callouts$pct_targeted, y = callouts$lift + 1, label = callouts$label,
color = INK, size = 3.2, fontface = "bold", hjust = 0
) +
scale_color_manual(
name = NULL,
values = c("Model" = IMPRINT_PALETTE[1], "Random baseline" = INK_SOFT)
) +
scale_x_continuous(labels = scales::label_percent(scale = 1)) +
labs(
title = "Fraud Detection Model · lift-curve · r · ggplot2 · anyplot.ai",
x = "Population Targeted",
y = "Cumulative Lift"
) +
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.major.y = element_line(color = GRID_LINE, linewidth = 0.3),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank(),
axis.title = element_text(color = INK, size = 10),
axis.text = element_text(color = INK_SOFT, size = 8),
axis.ticks = element_blank(),
plot.title = element_text(color = INK, size = 12),
legend.position = "inside",
legend.position.inside = c(0.98, 0.94),
legend.justification = c(1, 1),
legend.background = element_rect(fill = ELEVATED_BG, color = INK_SOFT, linewidth = 0.3),
legend.margin = margin(t = 8, r = 8, b = 8, l = 8),
legend.key = element_blank(),
legend.text = element_text(color = INK_SOFT, size = 8)
)
# --- 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/lift-curve/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": "lift-curve",
"language": "r",
"library": "ggplot2",
"page": "https://anyplot.ai/lift-curve/r/ggplot2",
"hub": "https://anyplot.ai/lift-curve",
"code_json": "https://api.anyplot.ai/specs/lift-curve/ggplot2/code",
"spec_json": "https://api.anyplot.ai/specs/lift-curve",
"render_light_png": "https://storage.googleapis.com/anyplot-images/plots/lift-curve/r/ggplot2/plot-light.png",
"render_dark_png": "https://storage.googleapis.com/anyplot-images/plots/lift-curve/r/ggplot2/plot-dark.png",
"quality_score": 92.0,
"license": "MIT",
"guide": "https://anyplot.ai/llms.txt"
}Part of Model Lift Chart on anyplot.ai.