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AI / MLAvançadoVerificado
ML Model Trainer
85
Trains ML models with scikit-learn, XGBoost or PyTorch. Includes feature engineering, cross-validation and hyperparameter tuning.
machine-learningtrainingscikit-learnpytorchxgboost
Linguagens
Python
Documento do Skill
SKILL.mdml-model-trainer/workflow
1
SENSE — Understand the problem
Classification vs regression vs clustering
Success metrics (accuracy, F1, RMSE)
2
CONTEXTUALIZE — Prepare data
Stratified train/val/test split
Feature engineering and selection
3
HYPOTHESIZE — Select candidate models
Simple baseline first (LogReg, DecisionTree)
Advanced models (XGBoost, LightGBM, Neural Net)
4
EVALUATE — Train and compare
K-fold cross-validation
Hyperparameter tuning (Optuna, GridSearch)
Learning curves and overfitting check
5
ACT — Finalize model
Train on full dataset
Serialize (pickle, ONNX, TorchScript)
6
REFLECT — Document
Feature importance
Confusion matrix and classification report
Model card with known limitations
Telemetria de Agentes
Execuções
12.4K
total
Taxa de Sucesso
83%
últimos 30d
Latência Média
8.5s
p50
Alucinação
5.0%
detecção
Uso por Plataforma
cursor7.2K
claude-code5.2K
Árvore do Skill
ML Model Trainer
ml-model-trainer
Fases Cognitivas6
1.SENSE
2.CONTEXTUALIZE
3.HYPOTHESIZE
4.EVALUATE
5.ACT
6.REFLECT
Triggers4
treinar modelomachine learningcriar modelo mltrain model
MCP Servers2
⚙️
Jupyter MCP
Notebook execution, Python cells and data visualization
AutoLocal Jupyter server
🔌
OpenAI MCP
Embedding generation, completions and moderation via OpenAI API
https://api.openai.com/v1/mcpConfigConfigure OPENAI_API_KEY
Skills Associados2
Avaliar este Skill
Score Breakdown
⭐Avaliação Humana87%
🤖Sucesso de Agentes83%
🕐Atualidade88%
🔗Saúde de Dependências95%
🕸️Centralidade no Grafo60%
🛡️Segurança49%
CompositeScore = α·Humano + β·Agente + γ·Recência + δ·Deps + ε·Centralidade + ζ·Segurança
Instalação
$ skillschain install ml-model-trainer
$ skillschain use ml-model-trainer