GraphCanon updated Oct 2, 2026 · GitHub synced Oct 2, 2026
35views this month
Decision brief
model_search simplifies model architecture search by automating the process with predefined configurations focusing on binary classification tasks.
Good fit when
- When you want to streamline the selection of optimal model architectures for your specific data without manual tuning.
- If you prefer an automated approach that supports binary classification problems and leverages default specifications.
Avoid when
- Avoid if your project requires customization beyond what model_search offers through predefined configurations.
- Not ideal for tasks outside of binary classification which strictly uses a logits_dimension of 2.
Observed Jul 17, 2026 · Source: enrich:decision_facts
Verify the decision
Maintenance and security
Full trust report- Maintenance
- Archived (793d since push)
- As of Oct 2, 2026
- Provenance
- Not a fork · Organization account
- As of Oct 2, 2026
- Security (OSV)
- 268 low (268 low)
- As of Jul 11, 2026
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Backing
Company context for Google. Display-only - separate from trust and ranking.
- Company
- Google·GitHub org profile·Jul 11, 2026
- Employees
- 47,756·Wikidata (P1128 employees)·Jul 11, 2026
- Commercial model
- Pure OSS·GitHub org profile (public repos)·Jul 11, 2026
Install
pip install model_searchSimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
This is an automated machine learning library designed to find the best model architecture given specific data constraints and optimization goals, focusing on simplifying model training processes through predefined configurations and evaluation metrics.
Capability facts
- Languages
- python
Source: github.language · Oct 2, 2026
Categories
Graph entities
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Oct 2, 2026)
```python import model_searchSource link
Tags
README
Getting Started Let us start with the simplest case. You have a csv file where the features are numbers and you would like to run let AutoML find the best model architecture for you. Below is a code snippet for doing so: The above code will try 200 different models all binary cla...
For agents
This page has a .md twin and JSON over the API.