O'Reilly on Data

SEPTEMBER 19, 2023

While data scientists were no longer handling Hadoop-sized workloads, they were trying to build predictive models on a different kind of “large” dataset: so-called “unstructured data.” ” There’s as much Keras, TensorFlow, and Torch today as there was Hadoop back in 2010-2012. And it was good.

Machine Learning 339

Machine Learning Testing Modeling Cost-Benefit 339

AWS Big Data

JULY 28, 2023

To connect as a federated user with the Redshift provisioned cluster, you need to follow the steps in the previous section that detailed how to connect with Redshift Serverless and query the Data Catalog as a federated user using Query Editor V2 and a third-party SQL client. There are additional changes required in IAM policy.

Data Lake 96

Data Lake Data Governance Data Warehouse Modeling 96

Domino Data Lab

APRIL 21, 2021

Now that the class imbalance has been resolved, we can move forward with the actual model training. Model training. First, we define a function that will perform a grid search for the optimal hyperparameters of the classifier. In highly unbalanced datasets this interpretation could lead to poor predictions. References. [1]

Statistics 69

Statistics Machine Learning Modeling Metrics 69

The Unofficial Google Data Science Blog

MARCH 31, 2016

We have many routine analyses for which the sparsity pattern is closer to the nested case and lme4 scales very well; however, our prediction models tend to have input data that looks like the simulation on the right. Compact approximations to bayesian predictive distributions." Cambridge University Press, (2012). [4]

Modeling 40

Modeling Statistics Advertising Testing 40