Comprehensive Classification Model Evaluation and Visualization
Generates a comprehensive set of evaluation metrics and visualizations for classification models, including classification reports, confusion matrices, ROC curves (binary and multi-class One-vs-Rest), and density plots of predicted probabilities.
npx skills add ECNU-ICALK/AutoSkill --skill comprehensive-classification-model-evaluation-and-visualization --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Comprehensive Classification Model Evaluation and Visualization Generates a comprehensive set of evaluation metrics and visualizations for classification models, including classification reports, confusion matrices, ROC curves (binary and multi-class One-vs-Rest), and density plots of predicted probabilities. ## Prompt # Role & Objective You are a Machine Learning Evaluation Assistant. Your task is to generate a comprehensive set of evaluation metrics and visualizations for a given classification model's predictions. # Communication & Style Preferences - Output clear, formatted evaluation metrics (Classification Report). - Generate high-quality, labeled plots using Matplotlib and Seaborn. - Ensure code is modular and can be integrated into a larger script (e.g., main.py). # Operational Rules & Constraints - **Required Metrics**: Compute and print Classification Report, Precision Score, F1 Score, and Accuracy Score. - **Required Visualizations**: 1. Confusion Matrix Heatmap. 2. Predicted vs Actual Distribution Plot (Histogram/Density). 3. Density Plots of Predicted Probabilities (for each class). 4. ROC Curve: - For binary classification: Standard ROC curve with AUC. - For multi-c
- Prompt
- Triggers
What does the Comprehensive Classification Model Evaluation and Visualization skill do?
Generates a comprehensive set of evaluation metrics and visualizations for classification models, including classification reports, confusion matrices, ROC curves (binary and multi-class One-vs-Rest), and density plots of predicted probabilities.
How do I install it?
Run `npx skills add ECNU-ICALK/AutoSkill --skill comprehensive-classification-model-evaluation-and-visualization --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From ECNU-ICALK/AutoSkill, a repository with 539 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.