Insight

MARCH 12, 2020

There was only one problem: literary agents, the gatekeepers of the publishing industry, kept rejecting the book?—?often Using nothing more than a book’s synopsis, the AIgent can surface similar books, genre tags, and sales proxies. Data Collection The AIgent leverages book synopses and book metadata.

Modeling 59

Modeling Metadata Publishing Sales 59

The Unofficial Google Data Science Blog

JUNE 30, 2016

In an ideal world, experimentation through randomization of the treatment assignment allows the identification and consistent estimation of causal effects. Identification We now discuss formally the statistical problem of causal inference. We start by describing the problem using standard statistical notation.

Statistics 44

Statistics Optimization Modeling Experimentation 44

Occam's Razor

JUNE 6, 2007

I am thrilled to say that my book Web Analytics: An Hour A Day has been published and is now widely available. It has been such an amazing journey to write the book, and for it to come up almost exactly a year after I started this blog. Damini, Chirag and now the book! :). Part One: The book (my side of the story, details).

Analytics 60

Analytics Metrics Marketing Dashboards 60

Occam's Razor

JANUARY 9, 2012

If you are doing lame stuff, why try harder in an analytics context by asking for Economic Value or Visitor Loyalty or Conversation Rate or a thousand other super powerful and insightful metrics ? It could also make a few more dollars constantly pimping my two books in posts. Fill it with the best web metrics to measure success.

Marketing 140

Marketing B2B Measurement Risk 140

Domino Data Lab

AUGUST 22, 2019

Many thanks to Addison-Wesley Professional for providing the permissions to excerpt “Natural Language Processing” from the book, Deep Learning Illustrated by Krohn , Beyleveld , and Bassens. a book) into a list of discrete elements of language (e.g., and 2.6) [ in the book]. Introduction. Tokenization.

Deep Learning 111

Deep Learning Modeling Metrics Testing 111

Domino Data Lab

MAY 8, 2019

If your “performance” metrics are focused on predictive power, then you’ll probably end up with more complex models, and consequently less interpretable ones. They also require advanced skills in statistics, experimental design, causal inference, and so on – more than most data science teams will have.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

Domino Data Lab

AUGUST 26, 2019

This article provides an excerpt of “Tuning Hyperparameters and Pipelines” from the book, Machine Learning with Python for Everyone by Mark E. We’ve already introduced models, parameters, and hyperparameters in Section 5.2.2.4 [ in the book ]. in the book. ]. Introduction. In [1]: # setup. Models, Parameters, Hyperparameters.

Testing 79

Testing Modeling Machine Learning Metrics 79

Occam's Razor

MARCH 23, 2010

Part of it is fueled by a vocal minority genuinely upset that 10 years on we are still not a statistically powered bunch doing complicated analysis that is shifting paradigms. Because every tool uses its own sweet metrics definitions, cookie rules, session start and end rules and so much more. Part of it fueled by some Consultants.

Analytics 118

Analytics Consulting Metrics Experimentation 118