Does it ensure randomness in the split?

Yes, the skill ensures the splitting process is randomized to prevent ordering bias and ensure the training and testing sets are representative.

Does it handle imbalanced data?

The skill follows best practices and can incorporate stratification to ensure class distributions are maintained across your subsets.

What file formats does the Dataset Splitter support?

The skill primarily works with CSV files and standard tabular data formats commonly used in Python-based machine learning workflows.

How is the splitting performed?

The skill generates and executes Python code using industry-standard libraries like pandas and scikit-learn to partition your local data files.

Can I specify custom split ratios?

Yes, you can define specific percentages for training, validation, and testing sets (e.g., 70/15/15) directly in your natural language request.

Dataset Splitter

Name: Dataset Splitter
Author: intent-solutions-io

byintent-solutions-io

Ciencia de Datos y ML

Automates the partitioning of datasets into training, validation, and testing sets for machine learning workflows.

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The Dataset Splitter skill streamlines the data preparation phase of machine learning by automatically dividing datasets into training, validation, and testing subsets based on user-defined ratios. It generates and executes specialized Python code to handle local data files, ensuring data integrity through randomized splitting and best practices like stratification for imbalanced datasets. This skill is essential for developers and data scientists who need to quickly establish robust evaluation frameworks and partition data without manual coding or external manipulation.

Características Principales

Customizable split ratios via natural language commands
Support for training, validation, and testing set creation
Automated data partitioning for machine learning models
Python code generation and execution for data handling
Maintains data integrity and randomization during processing
0 GitHub stars

Casos de Uso

Preparing a CSV dataset for training a supervised machine learning model
Creating an 80/20 train-test split for quick model performance evaluation
Generating stratified subsets to maintain class distributions in imbalanced data

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Características Principales

Customizable split ratios via natural language commands
Support for training, validation, and testing set creation
Automated data partitioning for machine learning models
Python code generation and execution for data handling
Maintains data integrity and randomization during processing
0 GitHub stars

Casos de Uso

Preparing a CSV dataset for training a supervised machine learning model
Creating an 80/20 train-test split for quick model performance evaluation
Generating stratified subsets to maintain class distributions in imbalanced data