evaluating-machine-learning-models
A suite of metrics to assess, validate, and compare ML model performance.
Install
mkdir -p .claude/skills/evaluating-machine-learning-models && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/3096" && unzip -o skill.zip -d .claude/skills/evaluating-machine-learning-models && rm skill.zipInstalls to .claude/skills/evaluating-machine-learning-models
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
Build this skill allows AI assistant to evaluate machine learning modelsKey capabilities
- →Assess machine learning model performance
- →Compare performance of multiple models
- →Identify areas for model improvement
- →Validate model performance before deployment
- →Generate metrics like accuracy, precision, recall, and F1-score
- →Report key performance indicators
How it works
Claude analyzes the user's request to identify the model and metrics, then uses the /eval-model command within the model-evaluation-suite plugin to perform the evaluation.
Inputs & outputs
When to use evaluating-machine-learning-models
- →Comparing performance of two models
- →Validating model accuracy against test sets
- →Calculating F1-scores
About this skill
Model Evaluation Suite
Evaluate machine learning models using a comprehensive suite of metrics including accuracy, precision, recall, F1-score, and custom KPIs.
Overview
This skill empowers Claude to perform thorough evaluations of machine learning models, providing detailed performance insights. It leverages the model-evaluation-suite plugin to generate a range of metrics, enabling informed decisions about model selection and optimization.
How It Works
- Analyzing Context: Claude analyzes the user's request to identify the model to be evaluated and any specific metrics of interest.
- Executing Evaluation: Claude uses the
/eval-modelcommand to initiate the model evaluation process within themodel-evaluation-suiteplugin. - Presenting Results: Claude presents the generated metrics and insights to the user, highlighting key performance indicators and potential areas for improvement.
When to Use This Skill
This skill activates when you need to:
- Assess the performance of a machine learning model.
- Compare the performance of multiple models.
- Identify areas where a model can be improved.
- Validate a model's performance before deployment.
Examples
Example 1: Evaluating Model Accuracy
User request: "Evaluate the accuracy of my image classification model."
The skill will:
- Invoke the
/eval-modelcommand. - Analyze the model's performance on a held-out dataset.
- Report the accuracy score and other relevant metrics.
Example 2: Comparing Model Performance
User request: "Compare the F1-score of model A and model B."
The skill will:
- Invoke the
/eval-modelcommand for both models. - Extract the F1-score from the evaluation results.
- Present a comparison of the F1-scores for model A and model B.
Best Practices
- Specify Metrics: Clearly define the specific metrics of interest for the evaluation.
- Data Validation: Ensure the data used for evaluation is representative of the real-world data the model will encounter.
- Interpret Results: Provide context and interpretation of the evaluation results to facilitate informed decision-making.
Integration
This skill integrates seamlessly with the model-evaluation-suite plugin, providing a comprehensive solution for model evaluation within the Claude Code environment. It can be combined with other skills to build automated machine learning workflows.
Prerequisites
- Appropriate file access permissions
- Required dependencies installed
Instructions
- Invoke this skill when the trigger conditions are met
- Provide necessary context and parameters
- Review the generated output
- Apply modifications as needed
Output
The skill produces structured output relevant to the task.
Error Handling
- Invalid input: Prompts for correction
- Missing dependencies: Lists required components
- Permission errors: Suggests remediation steps
Resources
- Project documentation
- Related skills and commands
When not to use it
- →When the data used for evaluation is not representative of real-world data
- →When specific metrics of interest are not clearly defined
- →When context and interpretation of results are not provided
Prerequisites
Limitations
- →Evaluation data must be representative of real-world data
- →Context and interpretation of results are needed for informed decision-making
How it compares
This skill automates model evaluation with a suite of metrics, providing detailed performance insights, unlike manual evaluation which requires individual metric calculation.
Compared to similar skills
evaluating-machine-learning-models side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| evaluating-machine-learning-models (this skill) | 1 | 27d | Review | Intermediate |
| openjudge | 0 | 5mo | Review | Intermediate |
| llm-evaluation | 6 | 2mo | No flags | Advanced |
| evaluating-llms-harness | 3 | 7mo | Review | Advanced |
Try saying
Example prompts that trigger this skill in your AI assistant.
More by jeremylongshore
View all by jeremylongshore →You might also like
openjudge
agentscope-ai
>
llm-evaluation
wshobson
Implement comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking. Use when testing LLM performance, measuring AI application quality, or establishing evaluation frameworks.
evaluating-llms-harness
davila7
Evaluates LLMs across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag). Use when benchmarking model quality, comparing models, reporting academic results, or tracking training progress. Industry standard used by EleutherAI, HuggingFace, and major labs. Supports HuggingFace, vLLM, APIs.
llm-evaluation
H4D3ZS
Implement comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking. Use when testing LLM performance, measuring AI application quality, or establishing evaluation frameworks.
Plate Evaluation
ShArAvaNPai
Runs current model against validation set and returns JSON metrics with automated recommendations
model-change
connorkitchings
Use for prediction logic, feature usage, backtests, or metrics changes.