Custom Multiclass Logistic Regression with NumPy
Implement a multiclass logistic regression classifier from scratch using NumPy and Pandas, avoiding libraries like scikit-learn. The implementation uses a One-vs-Rest strategy to handle multiple classes (e.g., 0, 1, 2) and saves the trained model coefficients to a pickle file.
npx skills add ECNU-ICALK/AutoSkill --skill custom-multiclass-logistic-regression-with-numpy --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 with NumPy Implement a multiclass logistic regression classifier from scratch using NumPy and Pandas, avoiding libraries like scikit-learn. The implementation uses a One-vs-Rest strategy to handle multiple classes (e.g., 0, 1, 2) and saves the trained model coefficients to a pickle file. ## Prompt # Role & Objective You are a Python developer implementing a custom machine learning classifier. Your task is to write code for a multiclass logistic regression model from scratch using NumPy and Pandas, without using high-level libraries like scikit-learn. # Operational Rules & Constraints 1. **Implementation**: Implement the logistic regression functions manually: - `sigmoid(z)`: The activation function. - `cost_function(X, y, theta)`: Computes the cost (loss). - `gradient_descent(X, y, theta, alpha, iterations)`: Optimizes the parameters. 2. **Multiclass Handling**: Do not assume binary classification. Use a One-vs-Rest (OvR) strategy to handle multiple classes (e.g., 0, 1, 2). Train a separate binary classifier for each class. 3. **Data Preparation**: Load feature vectors (e.g., TF-IDF) and labels. Add an intercept term (column of ones) to the f
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What does the Custom Multiclass Logistic Regression with NumPy skill do?
Implement a multiclass logistic regression classifier from scratch using NumPy and Pandas, avoiding libraries like scikit-learn. The implementation uses a One-vs-Rest strategy to handle multiple classes (e.g., 0, 1, 2) and saves 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-with-numpy --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.