Menu
R umap package for UMAP. Use for Uniform Manifold Approximation and Projection visualization.
Installer avec Codex ou Claude Copiez ce prompt, collez-le dans Codex, Claude ou un autre assistant, puis laissez-le vérifier la page du skill et l'installer pour vous.
Basé sur la classification professionnelle SOC
| name | umap |
| description | R umap package for UMAP. Use for Uniform Manifold Approximation and Projection visualization. |
Uniform Manifold Approximation and Projection.
library(umap)
# Run UMAP
um <- umap(data)
# Results
um$layout # 2D coordinates
# Plot
plot(um$layout, col = labels, pch = 19)
# Custom configuration
config <- umap.defaults
config$n_neighbors <- 15
config$min_dist <- 0.1
config$metric <- "euclidean"
config$n_epochs <- 200
um <- umap(data, config = config)
um <- umap(data,
n_neighbors = 15, # Local neighborhood size
n_components = 2, # Output dimensions
metric = "euclidean",
n_epochs = 200,
min_dist = 0.1, # Minimum distance in embedding
spread = 1,
random_state = 42
)
# R implementation (default)
um <- umap(data, method = "naive")
# Python implementation (requires reticulate)
um <- umap(data, method = "umap-learn")
# Fit UMAP
um <- umap(train_data)
# Transform new data
new_coords <- predict(um, new_data)
# Compute distances
d <- as.matrix(dist(data))
# UMAP from distances
um <- umap(d, input = "dist")
# With labels
um <- umap(data, labels = labels)
library(ggplot2)
umap_df <- data.frame(
x = um$layout[, 1],
y = um$layout[, 2],
label = labels
)
ggplot(umap_df, aes(x, y, color = label)) +
geom_point(alpha = 0.7) +
theme_minimal() +
labs(title = "UMAP Projection")
# n_neighbors: larger = more global structure
# min_dist: smaller = tighter clusters
# Try different parameters
params <- expand.grid(
n_neighbors = c(5, 15, 50),
min_dist = c(0.01, 0.1, 0.5)
)
R language data analysis and visualization skill. Use when user asks to (1) run R scripts or code, (2) install/update R packages, (3) perform data analysis with R, (4) create visualizations with ggplot2/plotly, (5) statistical analysis, (6) data manipulation with tidyverse/dplyr/data.table. Triggers on keywords like "R语言", "R脚本", "ggplot", "tidyverse", "数据分析", "可视化".
R DALEX package for model explanations. Use for explaining complex machine learning models.
R iml package for interpretable ML. Use for model-agnostic interpretability methods.
R lime package for local explanations. Use for explaining individual predictions with local interpretable models.
R packages for ML interpretability. Use for explaining and interpreting machine learning models.
R vip package for variable importance. Use for computing and visualizing variable importance scores.