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

Domino Data Lab

OCTOBER 7, 2020

MANOVA, for example, can test if the heights and weights in boys and girls is different. This statistical test is correct because the data are (presumably) bivariate normal. In high dimensions the data assumptions needed for statistical testing are not met. The accuracy of any predictive model approaches 100%.

Statistics 145

Statistics Testing Predictive Modeling Big Data 145

Domino Data Lab

APRIL 21, 2021

This is to prevent any information leakage into our test set. 2f%% of the test set." 2f%% of the test set." Fraudulent transactions are 0.17% of the test set. 2f%% of the test set." Fraudulent transactions are 50.00% of the test set. Model training. Feature Engineering. References. [1]

Statistics 69

Statistics Machine Learning Modeling Metrics 69

The Unofficial Google Data Science Blog

MARCH 31, 2016

We compared the output of a random effects model to a penalized GLM solver with "Elastic Net" regularization (i.e. both L1 and L2 penalties; see [8]) which were tuned for test set accuracy (log likelihood). These large timing tests had roughly 500 million and 800 million training examples respectively. ICML, (2005). [3]

Modeling 40

Modeling Statistics Advertising Testing 40