The Unofficial Google Data Science Blog

NOVEMBER 17, 2020

On the one hand, basic statistical models (e.g. On the other hand, sophisticated machine learning models are flexible in their form but not easy to control. More knots make the learned feature transformation smoother and more capable of approximating any monotonic function.

Machine Learning 108

Machine Learning Deep Learning Modeling Interactive 108

Domino Data Lab

APRIL 21, 2021

Rules-based fraud detection (top) vs. classification decision tree-based detection (bottom): The risk scoring in the former model is calculated using policy-based, manually crafted rules and their corresponding weights. deep learning) there is no guaranteed explainability. It can be implemented as either unsupervised (e.g.

Statistics 69

Statistics Machine Learning Modeling Metrics 69

Domino Data Lab

MAY 8, 2019

That’s a risk in case, say, legislators – who don’t understand the nuances of machine learning – attempt to define a single meaning of the word interpret. For example, in the case of more recent deep learning work, a complete explanation might be possible: it might also entail an incomprehensible number of parameters.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

Domino Data Lab

JULY 9, 2019

When he retired in 2009 he had some time on his hands. You can sleep at night as a data scientician and you know you’re not building a random number generator, but the people from product, they don’t want to know just that you can predict who’s going to be at risk. Please help us make sense of it.”

Data Science 112

Data Science Machine Learning Advertising Modeling 112