model-evaluation-metrics

Model Evaluation Metrics - Auto-activating skill for ML Training. Triggers on: model evaluation metrics, model evaluation metrics Part of the ML Training skill category.

1.00x

Quality

Does it follow best practices?

Impact

92%

1.00x

Average score across 3 eval scenarios

Securityby

Passed

No known issues

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Evaluation results

92%

-6%

Customer Churn Model Evaluation

Classification metrics evaluation

Criteria

Without context

With context

Uses Python

100%

Uses pip for install

75%

Uses sklearn metrics

100%

Accuracy computed

100%

Precision and Recall

100%

F1 Score

100%

ROC-AUC metric

100%

Confusion matrix

100%

Output validation

100%

Production-ready structure

100%

Data preparation step

100%

94%

-2%

Model Selection for Fraud Detection

Experiment tracking and model comparison

Criteria

Without context

With context

Uses Python

100%

Uses pip or requirements.txt

50%

25%

Uses sklearn

100%

Multiple models trained

100%

Data preparation included

100%

Experiment tracking structure

100%

Metrics per experiment

100%

Comparison output

100%

Best model identified

100%

Production-ready structure

100%

Output validation

100%

90%

Predictive Maintenance: Energy Consumption Forecasting

Regression metrics and hyperparameter evaluation

Criteria

Without context

With context

Uses Python

100%

Uses pip for dependencies

Uses sklearn

100%

MSE or RMSE computed

100%

MAE computed

100%

R-squared computed

100%

Hyperparameter variation

100%

Hyperparameter impact tracked

100%

Data preparation step

66%

Production-ready structure

100%

Structured output

100%

Repository: jeremylongshore/claude-code-plugins-plus-skills
Commit: 3e83543

Evaluated: 30 days ago
Agent: Claude Code
Model: Claude Sonnet 4.6

Table of Contents

Customer Churn Model Evaluation Model Selection for Fraud Detection Predictive Maintenance: Energy Consumption Forecasting

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