| name | splitting-datasets |
| description | Process split datasets into training, validation, and testing sets for ML model development. Use when requesting "split dataset", "train-test split", or "data partitioning". Trigger with relevant phrases based on skill purpose.
|
| allowed-tools | Read, Write, Edit, Grep, Glob, Bash(cmd:*) |
| version | 1.0.0 |
| author | Jeremy Longshore <jeremy@intentsolutions.io> |
| license | MIT |
Dataset Splitter
This skill provides automated assistance for dataset splitter tasks.
Overview
This skill automates the process of dividing a dataset into subsets for training, validating, and testing machine learning models. It ensures proper data preparation and facilitates robust model evaluation.
How It Works
- Analyze Request: The skill analyzes the user's request to determine the dataset to be split and the desired proportions for each subset.
- Generate Code: Based on the request, the skill generates Python code utilizing standard ML libraries to perform the data splitting.
- Execute Splitting: The code is executed to split the dataset into training, validation, and testing sets according to the specified ratios.
When to Use This Skill
This skill activates when you need to:
- Prepare a dataset for machine learning model training.
- Create training, validation, and testing sets.
- Partition data to evaluate model performance.
Examples
Example 1: Splitting a CSV file
User request: "Split the data in 'my_data.csv' into 70% training, 15% validation, and 15% testing sets."
The skill will:
- Generate Python code to read the 'my_data.csv' file.
- Execute the code to split the data according to the specified proportions, creating 'train.csv', 'validation.csv', and 'test.csv' files.
Example 2: Creating a Train-Test Split
User request: "Create a train-test split of 'large_dataset.csv' with an 80/20 ratio."
The skill will:
- Generate Python code to load 'large_dataset.csv'.
- Execute the code to split the dataset into 80% training and 20% testing sets, saving them as 'train.csv' and 'test.csv'.
Best Practices
- Data Integrity: Verify that the splitting process maintains the integrity of the data, ensuring no data loss or corruption.
- Stratification: Consider stratification when splitting imbalanced datasets to maintain class distributions in each subset.
- Randomization: Ensure the splitting process is randomized to avoid bias in the resulting datasets.
Integration
This skill can be integrated with other data processing and model training tools within the Claude Code ecosystem to create a complete machine learning workflow.
Prerequisites
- Appropriate file access permissions
- Required dependencies installed
Instructions
- Invoke this skill when the trigger conditions are met
- Provide necessary context and parameters
- Review the generated output
- Apply modifications as needed
Output
The skill produces structured output relevant to the task.
Error Handling
- Invalid input: Prompts for correction
- Missing dependencies: Lists required components
- Permission errors: Suggests remediation steps
Resources
- Project documentation
- Related skills and commands
