training-machine-learning-models

This skill trains machine learning models using automated workflows. It analyzes datasets, selects appropriate model types (classification, regression, etc.), configures training parameters, trains the model with cross-validation, generates performance metrics, and saves the trained model artifact. Use this skill when the user requests to "train" a model, needs to evaluate a dataset for machine learning purposes, or wants to optimize model performance. The skill supports common frameworks like scikit-learn.

1.07x

Quality

49%

Does it follow best practices?

Impact

98%

1.07x

Average score across 9 eval scenarios

Securityby

Passed

No known issues

Optimize this skill with Tessl

npx tessl skill review --optimize ./backups/skills-migration-20251108-070147/plugins/ai-ml/ml-model-trainer/skills/ml-model-trainer/SKILL.md

Evaluation results

100%

12%

Customer Churn Prediction Model

Classification model training and evaluation

Criteria

Without context

With context

scikit-learn used

100%

Classification algorithm

100%

Cross-validation applied

100%

Accuracy metric reported

100%

Precision metric reported

100%

Recall metric reported

100%

Model artifact saved

100%

Target variable identified

100%

Model type determined

100%

Data cleaned before training

100%

12%

Property Valuation Model

Regression model training and evaluation

Criteria

Without context

With context

scikit-learn used

100%

Regression algorithm

100%

Cross-validation applied

100%

MSE metric reported

100%

R-squared metric reported

100%

Model artifact saved

100%

Target variable identified

100%

Model type determined

100%

Performance output file

100%

Data formatted before training

100%

Employee Attrition Risk Scorer

Data preprocessing, feature engineering, hyperparameter tuning

Criteria

Without context

With context

scikit-learn used

100%

Missing values handled

100%

Categorical encoding

100%

Feature engineering applied

100%

Hyperparameter tuning

100%

Cross-validation applied

100%

Best params documented

100%

Model artifact saved

100%

Performance metrics reported

100%

Target variable identified

100%

Customer Support Ticket Routing

Multi-class classification and metric reporting

Criteria

Without context

With context

scikit-learn used

100%

Classification algorithm

100%

Multi-class handling

100%

Cross-validation applied

100%

Accuracy metric reported

100%

Precision metric reported

100%

Recall metric reported

100%

Target variable identified

100%

Model type determined

100%

Data prepared before training

100%

Model artifact saved

100%

96%

Apartment Rent Prediction Service

Model persistence and inference workflow

Criteria

Without context

With context

scikit-learn used

100%

Correct model type

100%

Cross-validation applied

100%

MSE metric reported

100%

50%

R-squared metric reported

100%

Model artifact saved

100%

Inference script loads model

100%

Predictions produced

100%

Target variable identified

100%

Data prepared before training

100%

Performance output file

100%

95%

Residential Energy Consumption Forecasting

Algorithm selection and cross-validated comparison

Criteria

Without context

With context

scikit-learn used

100%

Regression algorithms chosen

100%

Multiple models compared

100%

Cross-validation applied

100%

MSE reported per model

50%

R-squared reported per model

100%

Best model identified

100%

Model artifact saved

100%

Target variable identified

100%

Data prepared before training

100%

Results written to file

100%

Support Ticket Routing Classifier

Text feature extraction and classification

Criteria

Without context

With context

scikit-learn used

100%

Text features extracted

100%

Classification algorithm

100%

Model type determined

100%

Cross-validation applied

100%

Accuracy metric reported

100%

Precision metric reported

100%

Recall metric reported

100%

Target variable identified

100%

Model artifact saved

100%

Data prepared before training

100%

94%

13%

Bike Rental Demand Forecasting

Regression with feature engineering

Criteria

Without context

With context

scikit-learn used

100%

Regression algorithm

100%

Model type determined

100%

Feature engineering applied

100%

Cross-validation applied

100%

MSE metric reported

30%

50%

R-squared metric reported

100%

Target variable identified

100%

Data prepared before training

100%

87%

Model artifact saved

100%

Performance output file

100%

Transaction Fraud Detection Model

Imbalanced dataset classification

Criteria

Without context

With context

scikit-learn used

100%

Classification algorithm

100%

Model type determined

100%

Cross-validation applied

100%

Stratified cross-validation

100%

Accuracy metric reported

100%

Precision metric reported

100%

Recall metric reported

100%

Target variable identified

100%

Data prepared before training

100%

Model artifact saved

100%

Performance output file

100%

Repository: jeremylongshore/claude-code-plugins-plus-skills
Commit: 13d35b8

Evaluated: about 2 months ago
Agent: Claude Code
Model: Claude Sonnet 4.6

Table of Contents

Customer Churn Prediction Model Property Valuation Model Employee Attrition Risk Scorer Customer Support Ticket Routing Apartment Rent Prediction Service Residential Energy Consumption Forecasting Support Ticket Routing Classifier Bike Rental Demand Forecasting Transaction Fraud Detection Model

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