evaluate-model
CommunityMeasure ML model performance with key metrics.
Data & Analytics#recall#data science#machine learning#metrics#accuracy#model evaluation#mojo#precision
Authormvillmow
Version1.0.0
Installs0
System Documentation
What problem does it solve?
Accurately measuring and comparing machine learning model performance requires selecting and calculating appropriate metrics, which can be complex and time-consuming.
Core Features & Use Cases
- Metric Selection: Guides the choice of appropriate metrics (accuracy, precision, recall, F1, AUC, MSE, MAE) for classification and regression tasks.
- Performance Assessment: Helps assess model performance on test/validation datasets and detect overfitting or underfitting.
- Use Case: After training a new machine learning model, use this skill to evaluate its performance on a test dataset, generating a report with key metrics like accuracy and a confusion matrix.
Quick Start
Use the evaluate-model skill to outline a Mojo struct for evaluating classification and regression models, returning key metrics.
Dependency Matrix
Required Modules
None requiredComponents
Standard package💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: evaluate-model Download link: https://github.com/mvillmow/ProjectOdyssey/archive/main.zip#evaluate-model Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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