What types of classification tasks does this skill support?

It supports any supervised learning task involving labeled data where the goal is to predict a categorical outcome, such as binary or multi-class classification.

Can I use this for deep learning tasks?

This skill is primarily focused on standard classification algorithms and builders. For specific deep learning architectures, you may need additional specialized plugins.

How do I trigger this skill in Claude Code?

Simply ask Claude to 'build a classifier', 'train a model', or 'predict categorical outcomes' while providing a dataset.

What performance metrics does the skill provide?

It generates comprehensive reports including accuracy, precision, recall, and F1-scores to help you evaluate the model's reliability.

Does this skill handle data cleaning?

Yes, the skill automates standard data preprocessing and validation steps to ensure the dataset is ready for model training.

Classification Model Builder

Name: Classification Model Builder
Author: intent-solutions-io

byintent-solutions-io

Data Science & ML

Automates the end-to-end workflow of building, training, and evaluating machine learning classification models from labeled datasets.

About

This skill empowers Claude to act as an automated data science assistant, streamlining the process of constructing supervised learning models. It handles complex tasks including data preprocessing, feature selection, model training, and hyperparameter tuning using the classification-model-builder plugin. By providing detailed performance reports with metrics like F1-score and precision, it ensures that developers can quickly deploy robust classifiers for tasks such as spam detection, sentiment analysis, or churn prediction.

Key Features

Intelligent hyperparameter tuning for performance optimization
Seamless integration with Python-based machine learning pipelines
Streamlined data preprocessing and feature extraction
Comprehensive evaluation reporting with accuracy, precision, and recall
0 GitHub stars
Automated model selection based on dataset characteristics

Use Cases

Predicting customer churn using historical behavioral data
Building a spam detection system from a raw email dataset
Developing categorical outcome models for medical or financial datasets

About

Key Features

Intelligent hyperparameter tuning for performance optimization
Seamless integration with Python-based machine learning pipelines
Streamlined data preprocessing and feature extraction
Comprehensive evaluation reporting with accuracy, precision, and recall
0 GitHub stars
Automated model selection based on dataset characteristics

Use Cases

Predicting customer churn using historical behavioral data
Building a spam detection system from a raw email dataset
Developing categorical outcome models for medical or financial datasets