Can I use this skill for both classification and regression?

Yes, the skill automatically analyzes your dataset to determine whether a classification or regression model is the most appropriate based on the target variable.

Does it save the trained models for later use?

Yes, the skill is designed to save the trained model artifact locally so you can use it for future predictions or production deployment.

How does the skill ensure model reliability?

It implements cross-validation techniques during training and generates comprehensive performance metrics such as accuracy, precision, recall, and R-squared.

Which machine learning frameworks does this skill support?

This skill primarily supports common Python frameworks like scikit-learn for handling classification and regression tasks.

Is manual data cleaning required before use?

While the skill performs analysis, it is a best practice to ensure your dataset is clean and properly formatted to achieve the highest model accuracy.

ML Model Trainer

Name: ML Model Trainer
Author: jeremylongshore

byjeremylongshore

•

883

データサイエンスとML

Automates the end-to-end machine learning workflow including data analysis, model selection, training, and performance evaluation.

概要

This skill empowers Claude to handle the complexities of machine learning model development by automating dataset analysis, algorithm selection, and hyperparameter configuration. It supports both classification and regression tasks using frameworks like scikit-learn, implementing best practices such as cross-validation and metrics generation to ensure robust results. It is ideal for developers and data scientists looking to quickly prototype, evaluate, and persist trained models within their local development environment.

主な機能

Built-in cross-validation and performance metrics generation
883 GitHub stars
Automatic persistence of trained model artifacts
Automated model selection for classification and regression tasks
Integrated data analysis and target variable identification
Support for popular ML frameworks like scikit-learn

ユースケース

Automating the evaluation of multiple model types to find the best fit for a dataset
Developing a regression model to predict housing market prices based on features
Training a customer churn classification model from a CSV dataset

概要

主な機能

Built-in cross-validation and performance metrics generation
883 GitHub stars
Automatic persistence of trained model artifacts
Automated model selection for classification and regression tasks
Integrated data analysis and target variable identification
Support for popular ML frameworks like scikit-learn

ユースケース

Automating the evaluation of multiple model types to find the best fit for a dataset
Developing a regression model to predict housing market prices based on features
Training a customer churn classification model from a CSV dataset