- Notifications
You must be signed in to change notification settings - Fork405
[DRAFT] guide: integrating chDB, Cloud and Scikit learn#4700
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to ourterms of service andprivacy statement. We’ll occasionally send you account related emails.
Already on GitHub?Sign in to your account
Draft
Blargian wants to merge5 commits intoClickHouse:mainChoose a base branch
base:main
Could not load branches
Branch not found:{{ refName }}
Loading
Could not load tags
Nothing to show
Loading
Are you sure you want to change the base?
Some commits from the old base branch may be removed from the timeline, and old review comments may become outdated.
Uh oh!
There was an error while loading.Please reload this page.
Conversation
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.Learn more about bidirectional Unicode characters
@Blargian is attempting to deploy a commit to theClickHouse Team onVercel. A member of the Team first needs toauthorize it. |
vercelbot commentedNov 5, 2025 • edited
Loading Uh oh!
There was an error while loading.Please reload this page.
edited
Uh oh!
There was an error while loading.Please reload this page.
The latest updates on your projects. Learn more aboutVercel for GitHub.
|
| import confusion_matrix from '@site/static/images/use-cases/AI_ML/Scikit/confusion_matrix.png'; | ||
| # Classifying UK property types with chDB and scikit-learn | ||
Collaborator
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others.Learn more.
Suggested change
| :::note[TL;DR] | |
| This guide demonstrates how chDB complements scikit-learn for ML workflows by building a binary classifier that predicts UK property types. You'll learn how to: | |
| - Use chDB for fast feature engineering on 11.8M records from ClickHouse Cloud | |
| - Build and train a Random Forest classifier achieving~87% accuracy | |
| - Deploy the model back to ClickHouse via UDFs for real-time inference | |
| The pattern shown here applies to any binary classification problem where you need efficient data preprocessing at scale. | |
| Time required: 45-60 minutes | |
| ::: |
Sign up for freeto join this conversation on GitHub. Already have an account?Sign in to comment
Labels
None yet
2 participants
Add this suggestion to a batch that can be applied as a single commit.This suggestion is invalid because no changes were made to the code.Suggestions cannot be applied while the pull request is closed.Suggestions cannot be applied while viewing a subset of changes.Only one suggestion per line can be applied in a batch.Add this suggestion to a batch that can be applied as a single commit.Applying suggestions on deleted lines is not supported.You must change the existing code in this line in order to create a valid suggestion.Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be applied on multi-line comments.Suggestions cannot be applied while the pull request is queued to merge.Suggestion cannot be applied right now. Please check back later.
Summary
Adds a guide which details how you can use Cloud, chDB and Scikit learn together to train a model and run inference.
Covers:
To do:
Checklist