Remove 2012 Remove Optimization Remove Predictive Modeling
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Structural Evolutions in Data

O'Reilly on Data

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.

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Simplify external object access in Amazon Redshift using automatic mounting of the AWS Glue Data Catalog

AWS Big Data

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.

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Credit Card Fraud Detection using XGBoost, SMOTE, and threshold moving

Domino Data Lab

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]

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Using random effects models in prediction problems

The Unofficial Google Data Science Blog

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]