---
title: "XGBoost"
description: "2 posts about XGBoost, summarised, each linking to the original."
---

# XGBoost
> 2 posts about XGBoost, summarised, each linking to the original.

## Articles

### [Putting a Transformer Inside Every Card Authorization](https://yomu.fyi/post/putting-a-transformer-inside-every-card-authorization.md)
- Company: [Ramp](https://yomu.fyi/company/ramp.md)
- Author: Dwight Temple, Simon Miller
- Published: Oct 7, 2026
- Type: Problem & solution

Ramp’s new card fraud model combines a small transformer pre-trained without fraud labels with an XGBoost stage to score each authorization in real time. The transformer reads a cardholder’s recent transactions and learns to predict the next transaction, producing embeddings and surprisal scores that capture how anomalous each field is. The model caught 32% more fraud dollars than its predecessor while flagging the same number of transactions, with average precision 12% higher than XGBoost alone. Most of the dollar gain comes from weighting XGBoost training examples by dollar amount, while the transformer improves ranking and catches more first fraudulent charges on a card. The transformer trains in under 90 minutes on four L40S GPUs and runs on CPU within the authorization latency budget after optimizations cut median model step time from 145 to 54 milliseconds.


### [Validating Bounding Box Annotations](https://yomu.fyi/post/validating-bounding-box-annotations.md)
- Company: [Uber](https://yomu.fyi/company/uber.md)
- Published: Apr 30, 2026
- Type: Explainer

Video bounding-box annotations can contain ID swaps and position jumps that degrade training data for object-tracking models, especially when long videos are split among annotators and later merged. Uber's system runs in uLabel, combining visual, motion, and coordinate features from an 11-frame sliding window with an XGBoost classifier trained on synthetic anomalies from six open-source datasets. It scores each frame for error probability, then groups predictions with frame-index and confidence clustering into regions that operators can review, fix, or dismiss. The current iteration prioritizes jump and ID-swap detection, while freeze and scale/aspect errors remain planned for future releases; the deployed workflow is intended to reduce the cost and time of sequential edit-review while processing client data statelessly without retaining it or using it to train models.