Scikit Learn
zLanqing/codex-claude-academic-skills
Machine learning in Python with scikit-learn. An agent skill from zLanqing/codex-claude-academic-skills.
ML model selection matrix by problem type, hyperparameter tuning strategies, and ensemble methodology guide.
Install Model Selection Guide
$ npx skills add revfactory/harness-100 --skill model-selection-guide -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install revfactory/harness-100 model-selection-guide --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/revfactory/harness-100.git skills-src && mkdir -p .claude/skills && cp -r skills-src/en/31-ml-experiment/.claude/skills/model-selection-guide .claude/skills/model-selection-guide && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "model-selection-guide" agent skill from https://github.com/revfactory/harness-100/tree/main/en/31-ml-experiment/.claude/skills/model-selection-guide into .claude/skills/model-selection-guide/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "model-selection-guide", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/revfactory/harness-100/tree/main/en/31-ml-experiment/.claude/skills/model-selection-guideType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add revfactory/harness-100 --skill model-selection-guide -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install revfactory/harness-100 model-selection-guide --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/revfactory/harness-100.git skills-src && mkdir -p .agents/skills && cp -r skills-src/en/31-ml-experiment/.claude/skills/model-selection-guide .agents/skills/model-selection-guide && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "model-selection-guide" agent skill from https://github.com/revfactory/harness-100/tree/main/en/31-ml-experiment/.claude/skills/model-selection-guide into .agents/skills/model-selection-guide/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "model-selection-guide", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add revfactory/harness-100 --skill model-selection-guide -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install revfactory/harness-100 model-selection-guide --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/revfactory/harness-100.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/en/31-ml-experiment/.claude/skills/model-selection-guide .cursor/skills/model-selection-guide && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "model-selection-guide" agent skill from https://github.com/revfactory/harness-100/tree/main/en/31-ml-experiment/.claude/skills/model-selection-guide into .cursor/skills/model-selection-guide/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "model-selection-guide", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/revfactory/harness-100.git --path en/31-ml-experiment/.claude/skills/model-selection-guide--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add revfactory/harness-100 --skill model-selection-guide -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install revfactory/harness-100 model-selection-guide --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/revfactory/harness-100.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/en/31-ml-experiment/.claude/skills/model-selection-guide .gemini/skills/model-selection-guide && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "model-selection-guide" agent skill from https://github.com/revfactory/harness-100/tree/main/en/31-ml-experiment/.claude/skills/model-selection-guide into .gemini/skills/model-selection-guide/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "model-selection-guide", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install revfactory/harness-100 model-selection-guideInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add revfactory/harness-100 --skill model-selection-guide -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/revfactory/harness-100.git skills-src && mkdir -p .github/skills && cp -r skills-src/en/31-ml-experiment/.claude/skills/model-selection-guide .github/skills/model-selection-guide && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "model-selection-guide" agent skill from https://github.com/revfactory/harness-100/tree/main/en/31-ml-experiment/.claude/skills/model-selection-guide into .github/skills/model-selection-guide/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "model-selection-guide", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add revfactory/harness-100 --skill model-selection-guide -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install revfactory/harness-100 model-selection-guide --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/revfactory/harness-100.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/en/31-ml-experiment/.claude/skills/model-selection-guide .opencode/skills/model-selection-guide && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "model-selection-guide" agent skill from https://github.com/revfactory/harness-100/tree/main/en/31-ml-experiment/.claude/skills/model-selection-guide into .opencode/skills/model-selection-guide/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "model-selection-guide", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
model-selection-guide- GitHub stars
- 1.3k
- Token cost
- ~1.2k tokens
- SKILL.md length
- 203 words
- Files
- 1
- Skills in repo
- 464
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
ML model selection matrix by problem type, hyperparameter tuning strategies, and ensemble methodology guide.
- ML model selection and design involving model selection
- SKILL.md covers Model Recommendations by…, XGBoost vs LightGBM vs CatBoost, Hyperparameter Tuning and Cross-Validation Strategies, plus 2 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Algorithm comparison
What it does
Model Selection Guide is an agent skill from revfactory/harness-100. ML model selection matrix by problem type, hyperparameter tuning strategies, and ensemble methodology guide. Use this skill for ML model selection and design involving 'model selection', 'algorithm comparison', 'hyperparameter tuning', 'Optuna', 'ensemble', 'XGBoost vs LightGBM', 'model comparison', 'cross-validation', etc. Enhances the model-designer and evaluation-analyst's model design capabilities. Note: data preprocessing and training infrastructure management are outside this skill's scope.
Its SKILL.md is about 1.2k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Data & Analytics, covering Machine learning. The licence is Apache-2.0.
When your agent uses it
- ML model selection and design involving model selection
- Algorithm comparison
- Hyperparameter tuning
- XGBoost vs LightGBM
Example prompts
- “model selection”
- “algorithm comparison”
- “hyperparameter tuning”
- “/model-selection-guide”
Requirements
- Python 3
What it can do on your machine
Read from SKILL.md and the folder at commit 8e8d35c. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md (its code samples are python).
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Model Selection Guide loads about 1.2k tokens when it runs. Until then it costs about 131 tokens; SKILL.md has 203 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from revfactory/harness-100 at commit 8e8d35c, republished under its Apache-2.0 licence (© revfactory). 203 words, ~1,218 tokens.
Download SKILL.mdSave it as
.claude/skills/model-selection-guide/SKILL.md (or your agent's skills folder).- name
- model-selection-guide
- description
- ML model selection matrix by problem type, hyperparameter tuning strategies, and ensemble methodology guide. Use this skill for ML model selection and design involving 'model selection', 'algorithm comparison', 'hyperparameter tuning', 'Optuna', 'ensemble', 'XGBoost vs LightGBM', 'model comparison', 'cross-validation', etc. Enhances the model-designer and evaluation-analyst's model design capabilities. Note: data preprocessing and training infrastructure management are outside this skill's scope.
Model Selection Guide — ML Model Selection Matrix Guide
Optimal model selection and tuning strategies based on problem type, data characteristics, and constraints.
Model Recommendations by Problem Type
Tabular Data
| Problem Type | Baseline | Best Candidates | Notes |
|---|---|---|---|
| Binary Classification | LogisticRegression | XGBoost, LightGBM | Tree-based usually optimal |
| Multi-class Classification | LogisticRegression(OVR) | LightGBM, CatBoost | CatBoost: many categoricals |
| Regression | LinearRegression | XGBoost, LightGBM | RandomForest: overfitting prevention |
| Ranking | — | LambdaMART (LightGBM) | Search/recommendation |
| Anomaly Detection | IsolationForest | AutoEncoder, LOF | Unsupervised/semi-supervised |
| Time Series | ARIMA | Prophet, LightGBM | Feature-based time-series: trees |
Unstructured Data
| Data | Model | Framework |
|---|---|---|
| Image | ResNet, EfficientNet, ViT | PyTorch, timm |
| Text | BERT, RoBERTa | HuggingFace Transformers |
| Audio | Whisper, Wav2Vec | HuggingFace |
| Graph | GCN, GAT | PyG, DGL |
XGBoost vs LightGBM vs CatBoost
| Criterion | XGBoost | LightGBM | CatBoost |
|---|---|---|---|
| Speed | Medium | Fast | Slow |
| Memory | High | Low | Medium |
| Categorical Handling | Encoding required | Built-in support | Best performance |
| Missing Value Handling | Built-in | Built-in | Built-in |
| Overfitting Prevention | regularization | GOSS, EFB | Ordered Boosting |
| GPU Support | ✅ | ✅ | ✅ |
| Default Recommendation | General purpose | Large data, speed priority | Many categoricals |
Hyperparameter Tuning
Optuna Basic Structure
import optuna
def objective(trial):
params = {
'n_estimators': trial.suggest_int('n_estimators', 100, 1000),
'max_depth': trial.suggest_int('max_depth', 3, 10),
'learning_rate': trial.suggest_float('learning_rate', 0.01, 0.3, log=True),
'subsample': trial.suggest_float('subsample', 0.6, 1.0),
'colsample_bytree': trial.suggest_float('colsample_bytree', 0.6, 1.0),
'reg_alpha': trial.suggest_float('reg_alpha', 1e-8, 10.0, log=True),
'reg_lambda': trial.suggest_float('reg_lambda', 1e-8, 10.0, log=True),
}
model = XGBClassifier(**params)
score = cross_val_score(model, X, y, cv=5, scoring='f1').mean()
return score
study = optuna.create_study(direction='maximize')
study.optimize(objective, n_trials=100)Tuning Priority
LightGBM tuning order:
Stage 1 (High impact): learning_rate, n_estimators, num_leaves
Stage 2 (Medium impact): max_depth, min_child_samples, subsample
Stage 3 (Low impact): reg_alpha, reg_lambda, colsample_bytree
Stage 4 (Fine-tuning): min_split_gain, path_smoothCross-Validation Strategies
| Strategy | Suitable For | Code |
|---|---|---|
| K-Fold | General (sufficient data) | KFold(n_splits=5) |
| Stratified K-Fold | Imbalanced classification | StratifiedKFold(n_splits=5) |
| Time Series Split | Time series | TimeSeriesSplit(n_splits=5) |
| Group K-Fold | Prevent group data leakage | GroupKFold(n_splits=5) |
| Repeated K-Fold | More stable estimation | RepeatedKFold(n_splits=5, n_repeats=3) |
Ensemble Methods
Stacking
from sklearn.ensemble import StackingClassifier
estimators = [
('xgb', XGBClassifier()),
('lgbm', LGBMClassifier()),
('cat', CatBoostClassifier(verbose=0)),
]
stack = StackingClassifier(
estimators=estimators,
final_estimator=LogisticRegression(),
cv=5
)Blending Weights
# Optimal weight search
from scipy.optimize import minimize
def objective(weights):
pred = sum(w * p for w, p in zip(weights, predictions))
return -f1_score(y_true, pred > 0.5)
result = minimize(objective, x0=[1/3]*3, constraints={'type': 'eq', 'fun': lambda w: sum(w)-1})Model Selection Decision Tree
Data type?
├── Tabular
│ ├── Rows < 1,000 → Logistic Regression / SVM
│ ├── 1,000 < Rows < 1M → XGBoost / LightGBM
│ └── Rows > 1M → LightGBM (speed priority)
├── Image → CNN (EfficientNet, ViT)
├── Text → Transformer (BERT)
└── Time Series
├── Univariate → Prophet / ARIMA
└── Multivariate → LightGBM (feature-based) / LSTM© revfactory, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in en/31-ml-experiment/.claude/skills/model-selection-guide of revfactory/harness-100.
Open the folder on GitHubat commit 8e8d35c
Compare with similar skills
Model Selection Guide next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Model Selection Guide this skillrevfactory/harness-100 | 1.3k | — | ~1.2k | Automated safety check: Pass | Apache-2.0 | |
| Scikit LearnzLanqing/codex-claude-academic-skills | 4.6k | 17 repos | ~3.9k | Automated safety check: Pass | BSD-3-Clause | |
| Senior Data ScientistRaidriar7170/hermes-skilleval | 125 | 6 repos | ~1.4k | Automated safety check: Pass | MIT | |
| Agentic Kaggle WorkflowFrankS-IntelLab/agentic-kaggle-skill | 188 | — | ~4k | Automated safety check: Pass | MIT | |
| Retention Analysisliangdabiao/claude-data-analysis-ultra-main | 290 | 1 repos | ~1.3k | Automated safety check: Notes | None | |
| Geomlitalo-goncalves/geoML | 108 | — | ~4.2k | Automated safety check: Pass | GPL-3.0 |
Similar skills
Senior Data Scientist
Raidriar7170/hermes-skilleval
World-class data science skill for statistical modeling, experimentation, causal inference, and advanced analytics.
Agentic Kaggle Workflow
FrankS-IntelLab/agentic-kaggle-skill
Takes a Kaggle competition from rules and validation design through baselines, ensembling and notebook architecture to a scored submission.
Retention Analysis
liangdabiao/claude-data-analysis-ultra-main
Analyze user retention and churn using survival analysis, cohort analysis, and machine learning.
Geoml
italo-goncalves/geoML
Working knowledge of the geoML Python package (github.com/italo-goncalves/geoML): variational Gaussian processes for spatial data, implicit geological modelling, block models, drillhole data…
Radiomics ML
Aperivue/medsci-skills
A skill your agent uses when building or auditing a radiomics or tabular clinical-ML prediction model with a classical learner (LASSO, SVM, random forest, XGBoost and similar).
More from revfactory/harness-100
All 464 skills in this repoAnti Bot Analyzer
revfactory/harness-100
A skill for analyzing website anti-bot defense mechanisms and developing legitimate evasion strategies.
API Error Design Patterns
revfactory/harness-100
Reference for designing how an API reports failures: structured error codes, response shapes, client-friendly messages, an error catalog and retry or fallback advice.
API Security Checklist
revfactory/harness-100
Walks a backend-dev agent through OWASP API Top 10 checks, authentication and authorization patterns, and defense code during API design.
Arg Parser Generator
revfactory/harness-100
Methodology for systematically designing and generating CLI tool argument parser structures.
Audience Segmentation
revfactory/harness-100
Audience segmentation skill used by the analyst and curator agents.
Audio Storytelling
revfactory/harness-100
Audio storytelling skill used by the podcast scriptwriter and show note editor.
Related
Categories
Questions about Model Selection Guide
What does Model Selection Guide do?
ML model selection matrix by problem type, hyperparameter tuning strategies, and ensemble methodology guide. Model Selection Guide is an agent skill from revfactory/harness-100. ML model selection matrix by problem type, hyperparameter tuning strategies, and ensemble methodology guide.
When should I use Model Selection Guide?
Model Selection Guide fits situations like: ML model selection and design involving model selection; algorithm comparison; hyperparameter tuning; XGBoost vs LightGBM.
How do I install Model Selection Guide in Claude Code?
Run `npx skills add revfactory/harness-100 --skill model-selection-guide -a claude-code`. Or copy the skill folder (en/31-ml-experiment/.claude/skills/model-selection-guide in revfactory/harness-100) into .claude/skills/model-selection-guide in your project. Claude Code loads it when a task matches its description.
How do I install Model Selection Guide in Codex?
Run `npx skills add revfactory/harness-100 --skill model-selection-guide -a codex`. Or copy the skill folder (en/31-ml-experiment/.claude/skills/model-selection-guide in revfactory/harness-100) into .agents/skills/model-selection-guide in your project. Codex loads it when a task matches its description.
Can I use Model Selection Guide in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add revfactory/harness-100 --skill model-selection-guide -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/model-selection-guide, .gemini/skills/model-selection-guide, .github/skills/model-selection-guide and .opencode/skills/model-selection-guide in your project.
What does Model Selection Guide need to run?
SKILL.md names no scripts, command-line tools or credentials: Model Selection Guide is instructions for the agent only. Our summary lists: Python 3.
Does Model Selection Guide access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Model Selection Guide safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Model Selection Guide use?
Model Selection Guide is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Model Selection Guide use?
About 1.2k tokens (SKILL.md is roughly 4.9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Model Selection Guide?
Skills that share tags, products or a category with Model Selection Guide: Scikit Learn (zLanqing/codex-claude-academic-skills, 4.6k stars), Senior Data Scientist (Raidriar7170/hermes-skilleval, 125 stars), Agentic Kaggle Workflow (FrankS-IntelLab/agentic-kaggle-skill, 188 stars) and Retention Analysis (liangdabiao/claude-data-analysis-ultra-main, 290 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Model Selection Guide?
revfactory (a GitHub user) maintains it in revfactory/harness-100, which has 1,290 GitHub stars. The repository holds 464 skills in this directory. The repository was last updated on March 22, 2026.
Source: revfactory/harness-100 on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.