Skill ディレクトリ

AI Agent のための再利用可能な Skill を見つける。

タスクで実際の GitHub Skill を検索し、利用前に Stars、Trust、監査、カテゴリ、インストール経路を確認できます。

すべての推奨は、リポジトリ、監査、インストール経路に明確につながっています。

検索結果: gradient-boosting

英語版ディレクトリ

Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow

28K
Stars
87/100
信頼
カテゴリ: ml-automation監査

A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.

18K
Stars
87/100
信頼
カテゴリ: ml-automation監査

A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

9.0K
Stars
86/100
信頼
カテゴリ: ml-automation監査

H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.

7.5K
Stars
86/100
信頼
カテゴリ: ml-automation監査

Optax is a gradient processing and optimization library for JAX.

2.3K
Stars
80/100
信頼
カテゴリ: ml-automation監査

Natural Gradient Boosting for Probabilistic Prediction

1.9K
Stars
80/100
信頼
カテゴリ: ml-automation監査

A long-form article / blog post — masthead, hero image placeholder, article body with figures and pull quotes, author byline, related posts. Use when the brief asks for "blog", "article", "post", "essay", or "case study".

90K
Stars
77/100
信頼
カテゴリ: design-creative監査

The most loved library for Bar, Line, Area, Pie, Donut, Stacked Bar, Population Pyramid, Radar, Bubble and Scatter charts in React Native. Allows 2D, 3D, gradient, animations and live data updates.

1.4K
Stars
83/100
信頼
カテゴリ: data-analysis監査

Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for large-scale datasets.

34K
Stars
77/100
信頼
カテゴリ: data-analysis監査

Generate highly simplified personified IP mascot logos with Flat-first geometry, rounded heavy forms, two IP colors plus one background color by default, and extremely subtle neo-skeuomorphic shading. Use when creating an animal, creature, robot, ghost, plant, object, or other character as a minimal square logo or app-icon artwork, including when the agent should infer three distinct IP directions from product-repository context.

2.0K
Stars
73/100
信頼
カテゴリ: automation監査

A design engineering system for AI coding agents to produce production-quality UI with craft-level standards.

272
Stars
69/100
信頼
カテゴリ: development監査

A minimal benchmark for scalability, speed and accuracy of commonly used open source implementations (R packages, Python scikit-learn, H2O, xgboost, Spark MLlib etc.) of the top machine learning algorithms for binary classification (random forests, gradient boosted trees, deep neural networks etc.).

1.9K
Stars
72/100
信頼
カテゴリ: ml-automation監査