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ix-supervised

Train and evaluate supervised machine learning models in Rust.

Updated Mar 12, 2026
Search Tags:#machine learning#evaluation#regression#classification#supervised learning
One-click install
npx skills add https://github.com/GuitarAlchemist/ix --skill ix-supervised
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ix-supervised
Source: https://github.com/GuitarAlchemist/ix/tree/main/.claude/skills/ix-supervised
Command: npx skills add https://github.com/GuitarAlchemist/ix --skill ix-supervised

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill streamlines the process of building, evaluating, and deploying supervised machine learning models. Whether for regression or classification tasks, it offers comprehensive tools and metrics for model training and assessment.

Core Features & Use Cases

  • Diverse Models: From linear regression and SVM to KNN and decision trees, supports a wide range of supervised learning algorithms.
  • Comprehensive Evaluation: Provides metrics like MSE, RMSE, and accuracy for model performance analysis.
  • Data Handling: Includes resampling techniques like SMOTE and undersampling to address imbalanced datasets.
  • Use Case: Ideal for a data scientist who needs to quickly set up, train, and test a variety of models on a given dataset.

Quick Start

Invoke the ix-supervised skill to train a new regression model using the data from 'sales_data.csv' and evaluate it.

Frequently Asked Questions about ix-supervised

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I train and evaluate supervised machine learning models for classification and regression?▼

To train and evaluate supervised machine learning models, you can use this Skill to build algorithms like linear regression, SVM, KNN, and decision trees, assessing performance with metrics such as MSE, RMSE, and accuracy.

What's the best way to handle imbalanced datasets when training classification models?▼

To handle imbalanced datasets during classification model training, apply resampling techniques like SMOTE and undersampling, which are integrated into the Skill to balance class distribution and improve model evaluation.

Do I need a Rust programming environment to build supervised learning models with this Skill?▼

Yes, building supervised learning models with this Skill requires a Rust programming environment and the ix-supervised library to execute training scripts and evaluate model performance.

Can I use cross-validation to assess regression model performance?▼

Yes, you can use cross-validation to assess regression model performance, as the Skill provides comprehensive evaluation mechanisms including cross-validation techniques to ensure reliable model assessment.

What supervised learning algorithms are supported for model training and evaluation?▼

Supported supervised learning algorithms include linear regression, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), naive Bayes, and decision trees, covering both classification and regression tasks.

Why use resampling techniques before evaluating machine learning models on skewed data?▼

Resampling techniques are used before evaluating machine learning models on skewed data to mitigate class imbalance, preventing biased predictions and ensuring metrics like accuracy reflect true model performance.

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