Data Science 101

OCTOBER 31, 2019

Data mining is the process of discovering these patterns among the data and is therefore also known as Knowledge Discovery from Data (KDD). The former is a term used for models where the data has been labeled, whereas, unsupervised learning, on the other hand, refers to unlabeled data. Classification.

Data mining 77

Data mining KDD Data Science Forecasting 77

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. 2] Ron Kohavi, Randal M.

Modeling 61

Modeling Experimentation Knowledge Discovery Testing 61

Domino Data Lab

DECEMBER 2, 2020

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. There are 2,000 red data points, and 1,000 blue data points, for reference. 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

For more on ad CTR estimation, refer to [2]. References [1] Omkar Muralidharan, Amir Najmi "Second Order Calibration: A Simple Way To Get Approximate Posteriors" , Technical Report, Google, 2015. [2] A machine learning system produces an estimated CTR $t_i$ for each query-ad pair. Our method has four steps: Bin by $t$.

KDD 40

KDD Testing Machine Learning Modeling 40