scikit-learn
Category: data-science-and-ml · License: BSD-3-Clause license · Version: 1.3 · Includes scripts
Tags: machine learning, scikit-learn, classification, regression, cross-validation
When to use it
Covers classical machine learning in Python with scikit-learn (sklearn): classification and regression estimators, clustering and dimensionality reduction, preprocessing, Pipeline and ColumnTransformer, cross-validation, metrics, and GridSearchCV hyperparameter tuning. Use when training or comparing classifiers and regressors on tabular or text data, clustering data and choosing the cluster count, building leakage-free preprocessing pipelines, evaluating models with cross-validation and metrics, or tuning hyperparameters. Not for deep learning; use a dedicated neural network framework instead.Example requests
Ask your agent in plain language, for example:- “random forest vs gradient boosting on my csv, which is better? use cross-val”
- “I have patients.csv with columns age, bmi, sex, smoker, region and a binary target ‘readmitted’ (about 8% positive). I’m doing StandardScaler on everything and then train_test_split, then LogisticRegression, and I get a ConvergenceWarning plus suspiciously high AUC. Can you fix this and set up a proper evaluation?”
- “I’ve got a table of 5,000 customers with about 20 numeric behavior measurements and no labels. I want to find natural groups and work out how many groups there really are, then show them in 2D.”
Install
/plugin marketplace add KalarisLabs/research-agent-skills and install the whole data-science-and-ml category with /plugin install data-science-and-ml@research-agent-skills.