DataRobot Blog

AUGUST 23, 2019

We started with the result of every match (and set scores) for ATP and WTA tour matches from 2010 through 2018. Once we had built this prediction model , we could take the draw of any tournament and simulate the results 100,000 times to find out how often each player would win with that particular draw.

Predictive Modeling 52

Predictive Modeling Modeling Marketing Analytics Data Science 52

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 256

Machine Learning Testing Modeling Cost-Benefit 256

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. Applied Stochastic Models in Business and Industry, 26 (2010): 639-658. [10] bandit problems).

Modeling 40

Modeling Statistics Advertising Testing 40