Remove AWS Remove Cross Validation Remove K-nearest Neighbors
article thumbnail

How IDIADA optimized its intelligent chatbot with Amazon Bedrock

AWS Machine Learning Blog

The integration with Amazon Bedrock is achieved through the Boto3 Python module, which serves as an interface to the AWS, enabling seamless interaction with Amazon Bedrock and the deployment of the classification model. Take the first step in your generative AI transformationconnect with an AWS expert today to begin your journey.

article thumbnail

Build a crop segmentation machine learning model with Planet data and Amazon SageMaker geospatial capabilities

AWS Machine Learning Blog

In late 2023, Planet announced a partnership with AWS to make its geospatial data available through Amazon SageMaker. In this analysis, we use a K-nearest neighbors (KNN) model to conduct crop segmentation, and we compare these results with ground truth imagery on an agricultural region.

professionals

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

Identifying defense coverage schemes in NFL’s Next Gen Stats

AWS Machine Learning Blog

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 81
article thumbnail

Understanding and Building Machine Learning Models

Pickl AI

K-Nearest Neighbors), while others can handle large datasets efficiently (e.g., Cross-Validation: Instead of using a single train-test split, cross-validation involves dividing the data into multiple folds and training the model on each fold. Some algorithms work better with small datasets (e.g.,