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doanchienthangdev
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training-data

Label, augment, and split machine learning training datasets.

4|1|Updated Dec 30, 2025
Search Tags:#mlops#machine learning#data labeling#data augmentation#data splitting#imbalanced data
One-click install
npx skills add https://github.com/doanchienthangdev/omgkit --skill training-data
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: training-data
Source: https://github.com/doanchienthangdev/omgkit/tree/main/plugin/skills/ml-systems/training-data
Command: npx skills add https://github.com/doanchienthangdev/omgkit --skill training-data

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, scikit-learn, nlpaug, albumentations, imblearn, torch, and includes scripts (resource) components.

What problem does it solve?

This Skill addresses the critical challenge of preparing high-quality training data for machine learning models, ensuring better model performance and reliability.

Core Features & Use Cases

  • Data Labeling: Supports manual labeling (e.g., for Label Studio) and weak supervision techniques using Snorkel.
  • Data Augmentation: Implements various augmentation strategies for text, images, and tabular data (e.g., SMOTE).
  • Imbalanced Data Handling: Provides methods like class weighting and focal loss to address skewed datasets.
  • Data Splitting: Offers robust splitting strategies including random, temporal, and group splits to prevent data leakage.
  • Use Case: You have an imbalanced dataset for a fraud detection model. Use this Skill to apply SMOTE for oversampling the minority class and then perform a stratified split to ensure both training and testing sets accurately represent the class distribution.

Quick Start

Use the training-data skill to perform a stratified split on your dataset with a 80/20 train/test ratio.

Frequently Asked Questions about training-data

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

FAQPage Schema
How do I handle imbalanced data for machine learning training?▼

Handle imbalanced data by applying oversampling techniques like SMOTE, class weighting, or focal loss to improve minority class representation and model performance.

What is the best way to split temporal data to prevent data leakage?▼

Prevent data leakage by using temporal or group splits instead of random splits, ensuring chronological integrity and keeping related records in the same data partition.

How do I augment text and image datasets for ML training?▼

Augment text and image datasets using nlpaug and albumentations to generate diverse training variations, improving model generalization and robustness across modalities.

Can I use weak supervision for data labeling with pandas and scikit-learn?▼

Yes, scale data annotation via weak supervision techniques to programmatically label large datasets, integrating seamlessly with pandas and scikit-learn pipelines.

Does this data splitting approach support stratified sampling for fraud detection?▼

Yes, stratified splitting maintains class distribution across training and testing sets, ensuring both partitions accurately represent minority classes in fraud detection.

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