Domino Data Lab

MAY 20, 2021

The dataset and code used in this blog post are available at [link] and all results shown here are fully reproducible, thanks to the Domino reproducibility engine, which is part of the Domino Data Science platform. Data mining for direct marketing: Problems and solutions. Protein classification with imbalanced data.

Machine Learning 136

Machine Learning Metrics Data mining Data Science 136

Domino Data Lab

JUNE 15, 2021

These companies often undertake large data science efforts in order to shift from “data-driven” to “model-driven” operations, and to provide model-underpinned insights to the business. The typical data science journey for a company starts with a small team that is tasked with a handful of specific problems.

Modeling 69

Modeling Data Science Data-driven Data Warehouse 69

Domino Data Lab

DECEMBER 4, 2020

This tutorial will show how easy it is to integrate and use Pumas in the Domino Data Science Platform , and we will carry out a simple non-compartmental analysis using a freely available dataset. The Domino data science platform empowers data scientists to develop and deliver models with open access to the tools they love.

Metrics 59

Metrics Data Science Knowledge Discovery Measurement 59

The Unofficial Google Data Science Blog

JULY 22, 2020

Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining. Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Henne, and Dan Sommerfield. 2] Scott, Steven L. armed bandit experiments in the online service economy."

Experimentation 96

Experimentation Statistics Testing Knowledge Discovery 96

The Unofficial Google Data Science Blog

OCTOBER 7, 2015

References [1] Henning Hohnhold, Deirdre O'Brien, Diane Tang, Focus on the Long-Term: It's better for Users and Business , Proceedings 21st Conference on Knowledge Discovery and Data Mining, 2015. [2] 2] Ron Kohavi, Randal M.

Modeling 63

Modeling Experimentation Knowledge Discovery KDD 63

The Unofficial Google Data Science Blog

JANUARY 14, 2016

by AMIR NAJMI Running live experiments on large-scale online services (LSOS) is an important aspect of data science. In this post we explore how and why we can be “ data-rich but information-poor ”. There are many reasons for the recent explosion of data and the resulting rise of data science.

Experimentation 42

Experimentation Statistics Metrics Measurement 42

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] 3] Bradley Efron, "Robbins, Empirical Bayes, and Microarrays" , Technical Report, 2003. [4]

KDD 40

KDD Testing Machine Learning Measurement 40

The Unofficial Google Data Science Blog

FEBRUARY 29, 2016

References [1] Diane Tang, Ashish Agarwal, Deirdre O’Brien, Mike Meyer, “ Overlapping Experiment Infrastructure: More, Better, Faster Experimentation ”, Proceedings 16th Conference on Knowledge Discovery and Data Mining, Washington, DC

Experimentation 41

Experimentation Statistics Metrics Measurement 41

Domino Data Lab

AUGUST 1, 2021

Instead, you should focus on how techniques like PDPs and LIME can be used to gain insights into the model’s inner workings and how you can add those to your data science toolbox. Conference on Knowledge Discovery and Data Mining, pp. References. Maria Fox, Derek Long, and Daniele Magazzeni. Ribeiro, M.

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139