Occam's Razor

APRIL 8, 2013

This thought was in my mind as I was reading Lean Analytics a new book by my friend Alistair Croll and his collaborator Benjamin Yoskovitz. First, you figure out what you want to improve; then you create an experiment; then you run the experiment; then you measure the results and decide what to do. Measure and decide what to do.

Metrics 156

Metrics KPI Analytics Key Performance Indicator 156

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

Domino Data Lab

OCTOBER 2, 2019

This article covers causal relationships and includes a chapter excerpt from the book Machine Learning in Production: Developing and Optimizing Data Science Workflows and Applications by Andrew Kelleher and Adam Kelleher. As data science work is experimental and probabilistic in nature, data scientists are often faced with making inferences.

Machine Learning 68

Machine Learning Measurement Modeling Testing 68

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

Most companies are astonishingly blasé about data and possibilities of measurement. " Sad, unimaginative measurements of their sad, unimaginative campaigns. It could also make a few more dollars constantly pimping my two books in posts. Allocate some of your aforementioned 15% budget to experimentation and testing.

Marketing 140

Marketing B2B Measurement Risk 140

O'Reilly on Data

MARCH 23, 2023

It has helped to write a book. Tokens ChatGPT’s sense of “context”—the amount of text that it considers when it’s in conversation—is measured in “tokens,” which are also used for billing. And it can look up an author and make statistical observations about their interests. And some of these things are mind blowing.

IT 262

IT Modeling Testing Risk 262

Domino Data Lab

MAY 8, 2019

Visualizations are vital in data science work, with the caveat that the information that they convey may be 4-5 layers of abstraction away from the actual business process being measured. measure the subjects’ ability to trust the models’ results. Use of influence functions goes back to the 1970s in robust statistics.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

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. Part of it fueled by some Consultants. I suppose the rational is: self preservation before all else. It has simply not had a break to catch a breath and mature.

Analytics 118

Analytics Consulting Metrics Experimentation 118

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