training-machine-learning-models

# Ml Model Trainer

This skill provides automated assistance for ml model trainer tasks.

## Overview

This skill empowers Claude to automatically train and evaluate machine learning models. It streamlines the model development process by handling data analysis, model selection, training, and evaluation, ultimately providing a persisted model artifact.

## How It Works

1. **Data Analysis and Preparation**: The skill analyzes the provided dataset and identifies the target variable, determining the appropriate model type (classification, regression, etc.).
2. **Model Selection and Training**: Based on the data analysis, the skill selects a suitable machine learning model and configures the training parameters. It then trains the model using cross-validation techniques.
3. **Performance Evaluation and Persistence**: After training, the skill generates performance metrics to evaluate the model's effectiveness. Finally, it saves the trained model artifact for future use.

## When to Use This Skill

This skill activates when you need to:
- Train a machine learning model on a given dataset.
- Evaluate the performance of a machine learning model.
- Automate the machine learning model training process.

## Examples

### Example 1: Training a Classification Model

User request: "Train a classification model on this dataset of customer churn data."

The skill will:
1. Analyze the customer churn data, identify the churn status as the target variable, and determine that a classification model is appropriate.
2. Select a suitable classification algorithm (e.g., Logistic Regression, Random Forest), train the model using cross-validation, and generate performance metrics such as accuracy, precision, and recall.

### Example 2: Training a Regression Model

User request: "Train a regression model to predict house prices based on features like size, location, and number of bedrooms."

The skill will:
1. Analyze the house price data, identify the price as the target variable, and dete