ml-data-fetch-annotate
OfficialFetch & annotate ML data for error analysis
Software Engineering#data fetching#machine learning#error analysis#pdf generation#ml#annotation#redshift
AuthorStream-claims
Version1.0.0
Installs0
System Documentation
What problem does it solve?
This Skill automates the process of fetching machine learning datasets from Redshift and generating annotated PDFs, streamlining the analysis of model errors and data quality.
Core Features & Use Cases
- Data Fetching: Query and retrieve segment or case data from Redshift for ML evaluation.
- PDF Annotation: Create annotated PDFs to visualize model predictions against ground truth and facilitate error analysis.
- Schema Exploration: Understand the data warehouse schema relevant to ML evaluation.
- Use Case: When a user needs to analyze why a classification model is making incorrect predictions, this skill can fetch the relevant data, generate reviewable PDFs highlighting the errors, and help identify patterns in misclassifications.
Quick Start
Fetch the latest 30 'BILL' segments for 'berkley_ent' and create an annotated PDF named 'segments.pdf'.
Dependency Matrix
Required Modules
pandasboto3PyMuPDF
Components
scriptsreferences
💻 Claude Code Installation
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Please help me install this Skill: Name: ml-data-fetch-annotate Download link: https://github.com/Stream-claims/stream-engineering/archive/main.zip#ml-data-fetch-annotate Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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