The Unofficial Google Data Science Blog

OCTOBER 7, 2015

Recently, we presented some basic insights from our effort to measure and predict long-term effects at KDD 2015 [1]. In this blog post, we summarize that paper and refer you to it for details.

Modeling 63

Modeling Experimentation Knowledge Discovery Testing 63

Domino Data Lab

DECEMBER 2, 2020

In this blog post, I will cover a family of techniques known as density-based clustering. Due to its importance in both theory and applications, this algorithm is one of three algorithms awarded the Test of Time Award at the KDD conference in 2014. Unlike k-means, DBSCAN does not require the number of clusters as a parameter.

Metrics 116

Metrics KDD Testing Machine Learning 116

The Unofficial Google Data Science Blog

NOVEMBER 4, 2015

Brendan McMahan et al, "Ad Click Prediction: a View from the Trenches" , Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2013. [3] References [1] Omkar Muralidharan, Amir Najmi "Second Order Calibration: A Simple Way To Get Approximate Posteriors" , Technical Report, Google, 2015. [2]

KDD 40

KDD Testing Machine Learning Measurement 40