2019, Clustering and Cross Validation

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2019

Clustering

Cross Validation

Identification of Hazardous Areas for Priority Landmine Clearance: AI for Humanitarian Mine Action

ML @ CMU

NOVEMBER 7, 2024

In close collaboration with the UN and local NGOs, we co-develop an interpretable predictive tool for landmine contamination to identify hazardous clusters under geographic and budget constraints, experimentally reducing false alarms and clearance time by half. Validation results in Colombia. RELand is our interpretable IRM model.

Clustering

Clustering Cross Validation Machine Learning Machine Learning

Identifying defense coverage schemes in NFL’s Next Gen Stats

AWS Machine Learning Blog

FEBRUARY 10, 2023

Quantitative evaluation We utilize 2018–2020 season data for model training and validation, and 2021 season data for model evaluation. We perform a five-fold cross-validation to select the best model during training, and perform hyperparameter optimization to select the best settings on multiple model architecture and training parameters.

ML ML Machine Learning Machine Learning

Webinars

Mastering Apache Airflow® 3.0: What’s New (and What’s Next) for Data Orchestration

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Identification of Hazardous Areas for Priority Landmine Clearance: AI for Humanitarian Mine Action

Identifying defense coverage schemes in NFL’s Next Gen Stats

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