Flipboard

JUNE 6, 2025

The code below will: Run 15+ models Evaluate them with cross-validation Return the best one based on performance All in two lines of code. clf = setup(data=df, target=df.columns[-1]) best_model = compare_models() As we can see here, PyCaret provides much more information about the model’s performance.

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Flipboard

FEBRUARY 2, 2023

Models were trained and cross-validated on the 2018, 2019, and 2020 seasons and tested on the 2021 season. To avoid leakage during cross-validation, we grouped all plays from the same game into the same fold. For more information on how to use GluonTS SBP, see the following demo notebook.

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Cross Validation ML ML Machine Learning 123

AWS Machine Learning Blog

OCTOBER 3, 2024

Steamlining model management and deployment with SageMaker Amazon SageMaker is a managed machine learning platform that provides data scientists and data engineers familiar concepts and tools to build, train, deploy, govern , and manage the infrastructure needed to have highly available and scalable model inference endpoints.

Data Science 95

Data Science AWS Machine Learning Machine Learning 95

AWS Machine Learning Blog

SEPTEMBER 29, 2023

The number of neighbors, a parameter greatly affecting the estimator’s performance, is tuned using cross-validation in KNN cross-validation. With over 15 years of experience, he supports customers globally in leveraging AI and ML for innovative solutions that capitalize on geospatial data. Shital Dhakal is a Sr.

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Machine Learning Machine Learning ML ML 138

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. Michael Chi is a Senior Director of Technology overseeing Next Gen Stats and Data Engineering at the National Football League. Each season consists of around 17,000 plays.

ML 98

ML ML Machine Learning Machine Learning 98

The MLOps Blog

DECEMBER 7, 2022

Quick shout out to the amazing data engineering team at CTF Capital, they really poured their hearts and brains into this! With all of that, the model gets retrained with all the data and stored in the Sagemaker Model Registry. After that, a chosen model gets deployed and used in the model pipeline.

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