Building Classification Models
About
This skill enables developers to automatically build and evaluate classification models using labeled datasets through the classification-model-builder plugin. It handles model creation, optimization, and performance reporting while following data validation and error handling best practices. Use it when you need to train classifiers or work on supervised learning tasks with structured data.
Quick Install
Claude Code
Recommended/plugin add https://github.com/jeremylongshore/claude-code-plugins-plusgit clone https://github.com/jeremylongshore/claude-code-plugins-plus.git ~/.claude/skills/Building Classification ModelsCopy and paste this command in Claude Code to install this skill
Documentation
Overview
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.
How It Works
- Context Analysis: Claude analyzes the user's request, identifying the dataset, target variable, and any specific requirements for the classification model.
- Model Generation: The skill utilizes the classification-model-builder plugin to generate code for training a classification model based on the identified dataset and requirements. This includes data preprocessing, feature selection, model selection, and hyperparameter tuning.
- Evaluation and Reporting: The generated model is trained and evaluated using appropriate metrics (e.g., accuracy, precision, recall, F1-score). Performance metrics and insights are then provided to the user.
When to Use This Skill
This skill activates when you need to:
- Build a classification model from a given dataset.
- Train a classifier to predict categorical outcomes.
- Evaluate the performance of a classification model.
Examples
Example 1: Building a Spam Classifier
User request: "Build a classifier to detect spam emails using this dataset."
The skill will:
- Analyze the provided email dataset to identify features and the target variable (spam/not spam).
- Generate Python code using the classification-model-builder plugin to train a spam classification model, including data cleaning, feature extraction, and model selection.
Example 2: Predicting Customer Churn
User request: "Create a classification model to predict customer churn using customer data."
The skill will:
- Analyze the customer data to identify relevant features and the churn status.
- Generate code to build a classification model for churn prediction, including data validation, model training, and performance reporting.
Best Practices
- Data Quality: Ensure the input data is clean and preprocessed before training the model.
- Model Selection: Choose the appropriate classification algorithm based on the characteristics of the data and the specific requirements of the task.
- Hyperparameter Tuning: Optimize the model's hyperparameters to achieve the best possible performance.
Integration
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.
GitHub Repository
Related Skills
sglang
MetaSGLang is a high-performance LLM serving framework that specializes in fast, structured generation for JSON, regex, and agentic workflows using its RadixAttention prefix caching. It delivers significantly faster inference, especially for tasks with repeated prefixes, making it ideal for complex, structured outputs and multi-turn conversations. Choose SGLang over alternatives like vLLM when you need constrained decoding or are building applications with extensive prefix sharing.
evaluating-llms-harness
TestingThis Claude Skill runs the lm-evaluation-harness to benchmark LLMs across 60+ standardized academic tasks like MMLU and GSM8K. It's designed for developers to compare model quality, track training progress, or report academic results. The tool supports various backends including HuggingFace and vLLM models.
content-collections
MetaThis skill provides a production-tested setup for Content Collections, a TypeScript-first tool that transforms Markdown/MDX files into type-safe data collections with Zod validation. Use it when building blogs, documentation sites, or content-heavy Vite + React applications to ensure type safety and automatic content validation. It covers everything from Vite plugin configuration and MDX compilation to deployment optimization and schema validation.
llamaguard
OtherLlamaGuard is Meta's 7-8B parameter model for moderating LLM inputs and outputs across six safety categories like violence and hate speech. It offers 94-95% accuracy and can be deployed using vLLM, Hugging Face, or Amazon SageMaker. Use this skill to easily integrate content filtering and safety guardrails into your AI applications.