The Unofficial Google Data Science Blog

DECEMBER 14, 2021

Some of that uncertainty is the result of statistical inference, i.e., using a finite sample of observations for estimation. But there are other kinds of uncertainty, at least as important, that are not statistical in nature. Representational uncertainty : the gap between the desired meaning of some measure and its actual meaning.

Uncertainty 56

Uncertainty Statistics Measurement Cost-Benefit 56

Ontotext

APRIL 3, 2024

W hen you try to automate business processes, semantics, and machine learning , knowledge graphs can bring a lot of value. So, this is a big driver for the outcome because when you are saving money for the business, you can measure it and see its value. These are not statistical inferences. What is a customer?

Technology 59

Technology Risk Digital Transformation Measurement 59

Domino Data Lab

DECEMBER 4, 2020

Domino Lab supports both interactive and batch experimentation with all popular IDEs and notebooks (Jupyter, RStudio, SAS, Zeppelin, etc.). TIME – time points of measured pain score and plasma concentration (in hrs). We can group by study arm and calculate various statistics as mean and standard deviation. pain_df.TIME.==

Metrics 59

Metrics Data Science Knowledge Discovery Measurement 59

IBM Big Data Hub

APRIL 18, 2024

LLMs like ChatGPT are trained on massive amounts of text data, allowing them to recognize patterns and statistical relationships within language. While AGI promises machine autonomy far beyond gen AI, even the most advanced systems still require human expertise to function effectively.

Cost-Benefit 90

Cost-Benefit Modeling Manufacturing Interactive 90

Insight

APRIL 28, 2020

You’ll often see the name “data challenge” used when the take-home assignment involves machine learning or statistics or “coding challenge” when the focus is on evaluating a candidate’s software engineering skills. provide an opportunity to measure both. Length: Highly Variable.

Data-driven 53

Data-driven Data Science Experimentation Measurement 53

Occam's Razor

JULY 24, 2018

To name a few: Digital Marketing & Measurement Model | Analytics Ecosystem | Web Analytics 2.0. During a discussion around planning for measurement, a peer was struggling with a unique collection of challenges. You see more digital metrics because digital is more measurable. Especially for the non-obvious problem #2 above.

Analytics 93

Analytics Metrics Strategy Measurement 93

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

Insight

MARCH 12, 2020

The most powerful approach for the first task is to use a ‘language model’ (LM), i.e. a statistical model of natural language. One way that we can get around this is to use the proportion of tags that fall into a given class as a measure of our degree of confidence in that class association. features) and metadata (i.e.

Modeling 59

Modeling Metadata Publishing Sales 59

Domino Data Lab

JUNE 23, 2019

Pete Skomoroch ’s “ Product Management for AI ”session at Rev provided a “crash course” on what product managers and leaders need to know about shipping machine learning (ML) projects and how to navigate key challenges. Be aware that machine learning often involves working on something that isn’t guaranteed to work.

Management 110

Management Machine Learning Experimentation Metrics 110

Domino Data Lab

MAY 8, 2019

I’ve been out themespotting and this month’s article features several emerging threads adjacent to the interpretability of machine learning models. Machine learning model interpretability. Other good related papers include: “ Towards A Rigorous Science of Interpretable Machine Learning ”. Not yet, if ever.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

Corinium

APRIL 25, 2019

For example, P&C insurance strives to understand its customers and households better through data, to provide better customer service and anticipate insurance needs, as well as accurately measure risks. Life insurance needs accurate data on consumer health, age and other metrics of risk.

Insurance 150

Insurance Risk IoT Cost-Benefit 150

The Unofficial Google Data Science Blog

OCTOBER 21, 2015

It is important to make clear distinctions among each of these, and to advance the state of knowledge through concerted observation, modeling and experimentation. Note also that this account does not involve ambiguity due to statistical uncertainty. We sliced and diced the experimental data in many many ways.

Slice and Dice 45

Slice and Dice Experimentation Data-driven Data Science 45

Cloudera

NOVEMBER 25, 2020

And soon also sensor measures, and possibly video or audio data with the increased use of device technology and telemedicine in medical care. How can he make it easy to see statistics, and do calculations, on discovered commonalities, across structured and unstructured data? Matthew’s company is not alone in this situation.

Data Warehouse 59

Data Warehouse Unstructured Data Analytics Visualization 59

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. Data scientists, machine learning (ML) researchers, and business stakeholders have a high-stakes investment in the predictive accuracy of models. Introduction.

Testing 79

Testing Modeling Machine Learning Metrics 79