Can I use this for both classification and regression?

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

Which machine learning frameworks does this skill support?

This skill primarily supports industry-standard Python-based frameworks like scikit-learn to handle classification and regression tasks.

Where are the trained models saved?

The skill saves a persisted model artifact (such as a joblib or pickle file) to your directory, allowing you to load and use the model for future inference.

What kind of performance metrics does it provide?

It provides standard metrics based on the task type, including accuracy, precision, and recall for classification, or Mean Squared Error (MSE) and R-squared for regression.

Does it handle data cleaning and preprocessing?

While the skill identifies target variables and prepares data, it is recommended that your dataset is clean and properly formatted before training for the best results.

ML Model Trainer

Name: ML Model Trainer
Author: jeremylongshore

byjeremylongshore

•

884

데이터 과학 및 ML

Automates the end-to-end machine learning workflow from dataset analysis and model selection to training and artifact persistence.

소개

This skill empowers Claude to act as an automated data scientist, streamlining the complex lifecycle of machine learning model development. It automatically detects task types such as classification or regression, selects optimal algorithms from common frameworks like scikit-learn, performs rigorous cross-validation, and generates comprehensive performance metrics. By handling the boilerplate of model selection and evaluation, it allows developers to quickly prototype, optimize, and save model artifacts for production use.

주요 기능

Generation of key performance metrics like accuracy, precision, and MSE
Automated dataset analysis and feature identification
Automated model type detection (classification vs. regression)
Persistence of trained model artifacts for future deployment
884 GitHub stars
Rigorous training using cross-validation techniques

사용 사례

Predicting customer churn using classification algorithms
Forecasting real estate prices with regression analysis
Automating the evaluation and benchmarking of different ML models

소개

주요 기능

Generation of key performance metrics like accuracy, precision, and MSE
Automated dataset analysis and feature identification
Automated model type detection (classification vs. regression)
Persistence of trained model artifacts for future deployment
884 GitHub stars
Rigorous training using cross-validation techniques

사용 사례

Predicting customer churn using classification algorithms
Forecasting real estate prices with regression analysis
Automating the evaluation and benchmarking of different ML models