Can I use this for both classification and regression?

Yes, the skill automatically detects the target variable type to determine whether a classification or regression approach is required.

How does the skill handle model evaluation?

It implements cross-validation techniques and generates detailed metrics such as accuracy, precision, recall, MSE, and R-squared to ensure reliability.

Which machine learning frameworks are supported?

The skill primarily supports industry-standard Python frameworks like scikit-learn for classification and regression tasks.

Does it save the trained model for later use?

Yes, the skill saves the trained model as a persisted artifact, making it ready for future inference or production deployment.

Is it compatible with TimeGPT pipelines?

While this skill handles general ML, it is designed to integrate within ecosystems that utilize Nixtla tools for time-series forecasting.

ML Model Trainer

Name: ML Model Trainer
Author: intent-solutions-io

byintent-solutions-io

数据科学与机器学习

Automates the end-to-end machine learning lifecycle from data analysis and model selection to training and evaluation.

关于

This skill transforms Claude into an automated data science assistant capable of streamlining the transition from raw datasets to production-ready models. By analyzing your data, it automatically determines the appropriate machine learning task—whether classification or regression—and handles the complexities of cross-validation, hyperparameter selection, and performance reporting. It is an essential tool for developers looking to rapidly prototype predictive features, benchmark model performance, or generate persisted artifacts for integration into larger software systems.

主要功能

Automated dataset analysis and target variable identification
0 GitHub stars
Support for classification and regression model architectures
Persistent artifact generation for model deployment
Integrated cross-validation and hyperparameter configuration
Comprehensive performance metrics including R-squared and F1-score

使用场景

Building a regression model to forecast house prices or demand
Automating the evaluation and benchmarking of different ML algorithms
Training a churn prediction classification model from customer datasets

关于

主要功能

Automated dataset analysis and target variable identification
0 GitHub stars
Support for classification and regression model architectures
Persistent artifact generation for model deployment
Integrated cross-validation and hyperparameter configuration
Comprehensive performance metrics including R-squared and F1-score

使用场景

Building a regression model to forecast house prices or demand
Automating the evaluation and benchmarking of different ML algorithms
Training a churn prediction classification model from customer datasets