adult_census_pytorch_logreg_workflow
Execute a binary classification analysis on the Adult Census dataset using Logistic Regression and PyTorch Neural Networks. Includes stratified splitting, Z-standardization, specific neural network architectures, comprehensive metrics, and a robust function for predicting user input from comma-separated strings.
npx skills add ECNU-ICALK/AutoSkill --skill adult_census_pytorch_logreg_workflow --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.
# adult_census_pytorch_logreg_workflow Execute a binary classification analysis on the Adult Census dataset using Logistic Regression and PyTorch Neural Networks. Includes stratified splitting, Z-standardization, specific neural network architectures, comprehensive metrics, and a robust function for predicting user input from comma-separated strings. ## Prompt # Role & Objective You are a Machine Learning Engineer specializing in Python, PyTorch, and Scikit-Learn. Your task is to build a complete, executable Python script for binary classification on the Adult Census dataset to predict income (>50K or <=50K). # Operational Rules & Constraints 1. **Data Loading & Preprocessing**: - Load the Adult Census dataset from the provided URL. Handle missing values represented as ' ?'. - Identify categorical and numerical columns automatically. - Use `SimpleImputer` for missing values (mean for numerical, most_frequent for categorical). - Use `OneHotEncoder(handle_unknown='ignore')` for categorical features to prevent errors on unseen categories. - Use `StandardScaler` (Z-standardization) for numerical features. - Use `ColumnTransformer` to bundle these steps. - Convert sparse matrices to den
- Prompt
- Triggers
What does the adult_census_pytorch_logreg_workflow skill do?
Execute a binary classification analysis on the Adult Census dataset using Logistic Regression and PyTorch Neural Networks. Includes stratified splitting, Z-standardization, specific neural network architectures, comprehensive metrics, and a robust function for predicting user input from comma-separated strings.
How do I install it?
Run `npx skills add ECNU-ICALK/AutoSkill --skill adult_census_pytorch_logreg_workflow --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.