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提供决策树分类建模与超参数优化能力；当用户需要建立分类模型、优化模型参数、生成可视化决策树或输出完整建模报告时使用
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
Convert files and office documents to Markdown. Supports PDF, DOCX, PPTX, XLSX, images (with OCR), audio (with transcription), HTML, CSV, JSON, XML, ZIP, YouTube URLs, EPubs and more.
Use this skill when users need to search academic papers, download research documents, extract citations, or gather scholarly information. Triggers include: requests to "find papers on", "search research about", "download academic articles", "get citations for", or any request involving academic databases like arXiv, PubMed, Semantic Scholar, or Google Scholar. Also use for literature reviews, bibliography generation, and research discovery. Requires OpenClawCLI installation from clawhub.ai.
Search academic papers and conduct literature reviews using OpenAlex API (free, no key needed). Use when the user needs to find scholarly papers by topic/author/DOI, explore citation chains, get structured paper metadata (title, authors, abstract, citations, DOI, open access URL), fetch full text of open access papers, or conduct automated literature reviews with theme identification and synthesis. Triggers on requests involving academic search, paper lookup, citation analysis, literature review, research synthesis, or scholarly reference gathering.
Use AI4Scholar author tools to search scholars, inspect author profiles, retrieve an author's papers, compare experts, identify potential reviewers, map labs or collaborators, and verify whether an author is the correct person.
Use AI4Scholar auto_cite to add real citations to academic text and return formatted references and BibTeX. Trigger for automatic citation insertion, APA/IEEE/Vancouver/Nature citation support, reference generation, BibTeX export, or checking whether claims have real supporting papers.
Use AI4Scholar to trace citation networks: citing papers, references, PubMed related papers, backward/forward citation search, classic paper discovery, mechanism literature expansion, and reviewer-style citation gap checks.
| name | decision-tree-modeling |
| description | 提供决策树分类建模与超参数优化能力；当用户需要建立分类模型、优化模型参数、生成可视化决策树或输出完整建模报告时使用 |
| dependency | {"python":["scikit-learn==1.3.0","pandas==2.0.3","numpy==1.24.3","matplotlib==3.7.2","openpyxl==3.1.2","joblib==1.3.1"]} |
数据准备
执行建模分析
scripts/decision_tree_modeling.py 进行建模--input: 输入数据文件路径--target: 目标列名--output_dir: 输出目录（默认：./output）--test_size: 测试集比例（默认：0.2）--cv_folds: 交叉验证折数（默认：5）--random_state: 随机种子（默认：42）python scripts/decision_tree_modeling.py --input ./iris.csv --target species --output_dir ./output
解读分析报告
output_dir/evaluation_report.jsonoutput_dir/decision_tree.pngoutput_dir/feature_importance.pngpython scripts/decision_tree_modeling.py \
--input ./iris.csv \
--target species \
--output_dir ./iris_output
python scripts/decision_tree_modeling.py \
--input ./data.xlsx \
--target category \
--cv_folds 10 \
--test_size 0.3
python scripts/decision_tree_modeling.py \
--input ./training_data.csv \
--target class_label \
--random_state 2024