Stats
Actions
Tags
From classification-model-builder
Builds and evaluates supervised classification models from labeled data using generated Python code. For spam detection, churn prediction, or similar tasks.
How this skill is triggered — by the user, by Claude, or both
Slash command
/classification-model-builder:building-classification-modelsThis skill is limited to the following tools:
The summary Claude sees in its skill listing — used to decide when to auto-load this skill
Build and evaluate classification models for supervised learning tasks with labeled data.
Build and evaluate classification models for supervised learning tasks with labeled data.
This skill empowers Claude to efficiently build and deploy classification models. It automates the process of model selection, training, and evaluation, providing users with a robust and reliable classification solution. The skill also provides insights into model performance and suggests potential improvements.
This skill activates when you need to:
User request: "Build a classifier to detect spam emails using this dataset."
The skill will:
User request: "Create a classification model to predict customer churn using customer data."
The skill will:
This skill integrates with the classification-model-builder plugin to automate the model building process. It can also be used in conjunction with other plugins for data analysis and visualization.
The skill produces structured output relevant to the task.
npx claudepluginhub jeremylongshore/claude-code-plugins-plus-skills --plugin classification-model-builderAutomates training ML models (classification, regression) on datasets: analyzes data, selects/configures algorithms, cross-validates, evaluates metrics, saves artifacts. Use for model training tasks.
Guides machine learning tasks in Python with scikit-learn: classification, regression, clustering, preprocessing, model evaluation, hyperparameter tuning, and ML pipelines.
Guides machine learning tasks in Python using scikit-learn for classification, regression, clustering, preprocessing, model evaluation, and pipelines.