القائمة
R lime package for local explanations. Use for explaining individual predictions with local interpretable models.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
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".
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R vip package for variable importance. Use for computing and visualizing variable importance scores.
R machine learning packages. Use for classification, regression, clustering, deep learning, gradient boosting (xgboost, lightgbm), random forests, neural networks, and time series forecasting.
| name | lime |
| description | R lime package for local explanations. Use for explaining individual predictions with local interpretable models. |
Local Interpretable Model-agnostic Explanations.
library(lime)
# Create explainer
explainer <- lime(
x = train_data,
model = model
)
# Explain single prediction
explanation <- explain(
x = new_data[1, ],
explainer = explainer,
n_features = 5
)
# Plot
plot_features(explanation)
# Explain multiple
explanation <- explain(
x = new_data[1:4, ],
explainer = explainer,
n_features = 5
)
# Plot all
plot_features(explanation)
# Plot explanations
plot_explanations(explanation)
explanation <- explain(
x = new_data,
explainer = explainer,
n_features = 5, # Number of features
n_labels = 1, # Number of labels (classification)
n_permutations = 5000, # Permutations for sampling
feature_select = "auto" # Feature selection method
)
# Methods
explanation <- explain(x, explainer, n_features = 5,
feature_select = "auto") # Automatic
explanation <- explain(x, explainer, n_features = 5,
feature_select = "forward_selection")
explanation <- explain(x, explainer, n_features = 5,
feature_select = "highest_weights")
explanation <- explain(x, explainer, n_features = 5,
feature_select = "lasso_path")
# For text classification
explainer <- lime(
x = train_text,
model = text_model,
preprocess = function(x) {
# Tokenize/vectorize text
}
)
explanation <- explain(
x = new_text,
explainer = explainer,
n_features = 10
)
# Highlight text
plot_text_explanations(explanation)
# For image classification
explainer <- lime(
x = train_images,
model = image_model,
preprocess = image_prep
)
explanation <- explain(
x = new_image,
explainer = explainer,
n_superpixels = 50,
weight = 10
)
plot_image_explanation(explanation)
# Define predict function
model_type.my_model <- function(x, ...) "classification"
predict_model.my_model <- function(x, newdata, ...) {
predict(x, newdata, type = "prob")
}
# Use with lime
explainer <- lime(train_data, my_model)