Genetic Algorithm Feature Selection and Comprehensive Model Evaluation
Implements a Genetic Algorithm (GA) using DEAP to select optimal features for a classification model (e.g., Breast Cancer Wisconsin), trains a Random Forest Classifier, and generates a comprehensive set of evaluation visualizations including Confusion Matrix, ROC Curves (binary and multi-class), Density Plots, and Predicted vs Actual distributions.
npx skills add ECNU-ICALK/AutoSkill --skill genetic-algorithm-feature-selection-and-comprehensive-model-eval --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.
# Genetic Algorithm Feature Selection and Comprehensive Model Evaluation Implements a Genetic Algorithm (GA) using DEAP to select optimal features for a classification model (e.g., Breast Cancer Wisconsin), trains a Random Forest Classifier, and generates a comprehensive set of evaluation visualizations including Confusion Matrix, ROC Curves (binary and multi-class), Density Plots, and Predicted vs Actual distributions. ## Prompt # Role & Objective You are an expert Machine Learning Engineer. Your task is to implement a Python script that performs feature selection using a Genetic Algorithm (GA), trains a classification model on the selected features, and generates a comprehensive set of evaluation visualizations. # Communication & Style Preferences - Provide the complete, executable Python code in a single block. - Use clear comments to explain the GA setup, data preprocessing, and plotting sections. - Ensure the code handles both binary and multi-class classification scenarios for ROC and density plots as requested. # Operational Rules & Constraints 1. **Data Preprocessing**: - Load the dataset from a CSV file (placeholder path). - Drop the 'id' column and any columns containing
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What does the Genetic Algorithm Feature Selection and Comprehensive Model Evaluation skill do?
Implements a Genetic Algorithm (GA) using DEAP to select optimal features for a classification model (e.g., Breast Cancer Wisconsin), trains a Random Forest Classifier, and generates a comprehensive set of evaluation visualizations including Confusion Matrix, ROC Curves (binary and multi-class), Density Plots, and Predicted vs Actual distributions.
How do I install it?
Run `npx skills add ECNU-ICALK/AutoSkill --skill genetic-algorithm-feature-selection-and-comprehensive-model-eval --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.