Does it include tools for model evaluation?

Absolutely. It features cross-validation templates and functions to compare multiple models using metrics like F1-score, ROC-AUC, and Accuracy.

Which machine learning algorithms are supported by this skill?

The skill supports popular algorithms including Logistic Regression, Random Forest, XGBoost, SVM, Ridge, and Lasso for both classification and regression tasks.

Can I perform hyperparameter optimization with this skill?

Yes, it includes specialized boilerplate for RandomizedSearchCV to efficiently tune parameters like tree depth, learning rates, and estimator counts.

How does the skill handle imbalanced datasets?

It provides implementation patterns for both over-sampling techniques like SMOTE and algorithmic adjustments such as balanced class weights.

Supervised Learning Model Builder

Name: Supervised Learning Model Builder
Author: pluginagentmarketplace

bypluginagentmarketplace

•

Data Science & ML

Builds, tunes, and evaluates production-ready classification and regression models using industry-standard machine learning algorithms.

The Supervised Learning skill empowers developers to implement end-to-end machine learning pipelines with production-grade reliability. It provides specialized patterns for classification and regression tasks, including automated hyperparameter tuning via RandomizedSearchCV and robust handling of class imbalance using SMOTE or weighted loss functions. By integrating best practices like stratified cross-validation and rigorous feature importance analysis, this skill ensures that models are not only accurate but also well-validated and ready for deployment with built-in observability for training metrics.

Key Features

01Sophisticated class imbalance handling with SMOTE and class weights

02Integrated logging for training time, CV scores, and model size

03Automated templates for Scikit-Learn and XGBoost implementations

04Advanced hyperparameter optimization and tuning patterns

05Comprehensive model comparison and performance benchmarking

061 GitHub stars

Use Cases

01Building regression models for price forecasting and demand estimation

02Optimizing existing machine learning models for improved accuracy and F1 scores

03Developing predictive classifiers for customer churn or fraud detection

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add pluginagentmarketplace/custom-plugin-machine-learning supervised-learning

For use in Claude.ai and ChatGPT

Download Skill

Key Features

01Sophisticated class imbalance handling with SMOTE and class weights

02Integrated logging for training time, CV scores, and model size

03Automated templates for Scikit-Learn and XGBoost implementations

04Advanced hyperparameter optimization and tuning patterns

05Comprehensive model comparison and performance benchmarking

061 GitHub stars

Use Cases

01Building regression models for price forecasting and demand estimation

02Optimizing existing machine learning models for improved accuracy and F1 scores

03Developing predictive classifiers for customer churn or fraud detection

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add pluginagentmarketplace/custom-plugin-machine-learning supervised-learning

For use in Claude.ai and ChatGPT

Download Skill