Can it handle large CSV files?

Yes, the skill is designed to process and partition large datasets, generating the necessary scripts to load and divide the data efficiently.

Can I specify custom split ratios?

Yes, you can request specific percentages for your training, validation, and testing sets, such as a 70/15/15 or 80/20 split.

Does this skill support stratification?

Yes, it follows best practices for data science, including stratification to maintain class distributions when dealing with imbalanced datasets.

How does this prevent data leakage?

By creating distinct, randomized partitions of your data, the skill ensures that your model is evaluated on data it has never seen during the training phase.

What libraries does the skill use to split data?

The skill generates Python code that utilizes standard industry libraries like scikit-learn and pandas to perform robust data manipulations.

Dataset Splitting & Partitioning

Name: Dataset Splitting & Partitioning
Author: intent-solutions-io

byintent-solutions-io

데이터 과학 및 ML

Automates the partitioning of datasets into training, validation, and testing sets to streamline machine learning model development.

소개

This skill empowers Claude to automatically handle the critical preprocessing step of data partitioning, allowing developers to divide raw datasets into optimized training, validation, and testing subsets. By generating and executing Python code using standard machine learning libraries, it ensures proper data integrity, randomization, and stratification, which are essential for robust model evaluation and preventing data leakage during the model training lifecycle.

주요 기능

Support for custom partitioning ratios (e.g., 80/20 or 70/15/15)
Automatic Python code generation using standard ML libraries
Data integrity verification and randomization
0 GitHub stars
Automated three-way splits for training, validation, and testing
Support for common data formats like CSV and large datasets

사용 사례

Creating dedicated validation sets for hyperparameter tuning
Preparing a raw CSV dataset for a supervised machine learning project
Partitioning data with stratification to handle imbalanced classes

소개

주요 기능

Support for custom partitioning ratios (e.g., 80/20 or 70/15/15)
Automatic Python code generation using standard ML libraries
Data integrity verification and randomization
0 GitHub stars
Automated three-way splits for training, validation, and testing
Support for common data formats like CSV and large datasets

사용 사례

Creating dedicated validation sets for hyperparameter tuning
Preparing a raw CSV dataset for a supervised machine learning project
Partitioning data with stratification to handle imbalanced classes