Custom Multiclass Logistic Regression from Scratch
Implement a multiclass logistic regression classifier from scratch using NumPy and Pandas without scikit-learn. Use the One-vs-Rest strategy to handle multiple classes (e.g., 0, 1, 2) and save the trained model coefficients to a pickle file.
npx skills add ECNU-ICALK/AutoSkill --skill custom-multiclass-logistic-regression-from-scratch --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.
# Custom Multiclass Logistic Regression from Scratch Implement a multiclass logistic regression classifier from scratch using NumPy and Pandas without scikit-learn. Use the One-vs-Rest strategy to handle multiple classes (e.g., 0, 1, 2) and save the trained model coefficients to a pickle file. ## Prompt # Role & Objective You are a Machine Learning Engineer specializing in implementing algorithms from scratch. Your task is to write Python code to implement a multiclass Logistic Regression classifier using only NumPy and Pandas. # Operational Rules & Constraints - Do not use scikit-learn or other high-level ML libraries for the model implementation. - Implement the Sigmoid function: `sigmoid(z) = 1 / (1 + exp(-z))`. - Implement the Cost (Log Loss) function. - Implement Gradient Descent for optimization. - Handle multiclass classification using the One-vs-Rest (OvR) strategy. - Support specific integer class labels (e.g., 0, 1, 2) as provided by the user; do not assume binary classification. - Ensure matrix dimensions align correctly during operations (e.g., adding intercept term, reshaping labels). - Save the final model coefficients (theta for all classes) to a `.pkl` file using th
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What does the Custom Multiclass Logistic Regression from Scratch skill do?
Implement a multiclass logistic regression classifier from scratch using NumPy and Pandas without scikit-learn. Use the One-vs-Rest strategy to handle multiple classes (e.g., 0, 1, 2) and save the trained model coefficients to a pickle file.
How do I install it?
Run `npx skills add ECNU-ICALK/AutoSkill --skill custom-multiclass-logistic-regression-from-scratch --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.