DataRobot

AUGUST 5, 2021

In behavioral science this is known as the blemish frame , where a small negative provides a frame of comparison to much stronger positives, strengthening the positive messaging. AI and Uncertainty. Some people react to the uncertainty with fear and suspicion. People are unsure about AI because it’s new. AI you can trust.

Uncertainty 98

Uncertainty Sales Risk Statistics 98

The Unofficial Google Data Science Blog

APRIL 19, 2021

by LEE RICHARDSON & TAYLOR POSPISIL Calibrated models make probabilistic predictions that match real world probabilities. Calibration applies in many applications, and hence the practicing data scientist must understand this useful tool. Why calibration matters What are the consequences of miscalibrated models? a sigmoid).

Modeling 122

Modeling IT Metrics Testing 122

Domino Data Lab

JUNE 2, 2019

Co-chair Paco Nathan provides highlights of Rev 2 , a data science leaders summit. We held Rev 2 May 23-24 in NYC, as the place where “data science leaders and their teams come to learn from each other.” If you lead a data science team/org, DM me and I’ll send you an invite to data-head.slack.com ”.

Data-driven 110

Data-driven Data Science Machine Learning Modeling 110

Smarten

JULY 18, 2022

By incorporating analytics into day-to-day activities and allowing access for business users, the business can encourage the transition from business user to Citizen Data Scientist and create a comprehensive system of analytics with governance and collaboration to ensure security, appropriate access, mobile use and fact-based decision-making.

Uncertainty 52

Uncertainty Predictive Analytics Forecasting Analytics 52

IBM Big Data Hub

APRIL 18, 2024

While these large language model (LLM) technologies might seem like it sometimes, it’s important to understand that they are not the thinking machines promised by science fiction. Most experts categorize it as a powerful, but narrow AI model. A key trend is the adoption of multiple models in production.

Cost-Benefit 96

Cost-Benefit Modeling Manufacturing Interactive 96

AWS Big Data

AUGUST 7, 2023

Many businesses use different software tools to analyze historical data and past patterns to forecast future demand and trends to make more accurate financial, marketing, and operational decisions. Forecasting acts as a planning tool to help enterprises prepare for the uncertainty that can occur in the future.

Forecasting 81

Forecasting Data Warehouse Machine Learning Modeling 81

Insight

FEBRUARY 19, 2020

Using variability in machine learning predictions as a proxy for risk can help studio executives and producers decide whether or not to green light a film project Photo by Kyle Smith on Unsplash Originally posted on Toward Data Science. Hollywood is a $10 billion-a-year industry, and movies range from huge hits to box office bombs.

Risk 67

Risk ROI Modeling Metrics 67

CIO Business Intelligence

JANUARY 4, 2023

Here are the shifts we noted: Delivery Models : Moving from an almost strictly on-premises systems approach (with some remote access services), HPC is embracing remote delivery models. Of course, data center challenges are also driving demand for these alternatives.

Cost-Benefit 59

Cost-Benefit Machine Learning Modeling Uncertainty 59

Sisense

SEPTEMBER 9, 2019

If you lack a data science team, integrating BigSquid with your open-platform BI tool is a powerful way to achieve the horsepower of data science while maintaining the ease of use that the average business user requires. Then we ran Kraken’s machine learning and predictive modeling engine to get the results.

Predictive Analytics 75

Predictive Analytics Predictive Modeling Forecasting Data-driven 75

Data Science and Beyond

JANUARY 7, 2019

Therefore, bootstrapping has been promoted as an easy way of modelling uncertainty to hackers who don’t have much statistical knowledge. Confidence intervals are a common way of quantifying the uncertainty in an estimate of a population parameter. Don’t compare confidence intervals visually.

Statistics 77

Statistics Uncertainty Testing Modeling 77

The Unofficial Google Data Science Blog

JULY 22, 2020

For this reason we don’t report uncertainty measures or statistical significance in the results of the simulation. In practice, one may want to use more complex models to make these estimates. For example, one may want to use a model that can pool the epoch estimates with each other via hierarchical modeling (a.k.a.

Experimentation 95

Experimentation Statistics Testing Strategy 95

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. We must therefore maintain statistical rigor in quantifying experimental uncertainty. In this post we explore how and why we can be “ data-rich but information-poor ”. And an LSOS is awash in data, right?

Experimentation 41

Experimentation Statistics Metrics Measurement 41

Domino Data Lab

JUNE 23, 2019

These measurement-obsessed companies have an advantage when it comes to AI. Google, Facebook, other leaders, they really have set up a culture of extreme measurement where every part of their product experience is instrumented to optimize clicks and drive user engagement. There’s no substitute for that experience.

Management 110

Management Machine Learning Experimentation Metrics 110

bridgei2i

JUNE 3, 2020

Consumers feel threatened by the prolonged uncertainty, not having had to deal with anything like it, in their lives. Difficulties in forecasting & planning: Pre-COVID forecasts are no longer valid as the pandemic has entirely disrupted the market and enterprises would need to work on new models to predict KPIs.

Manufacturing 52

Manufacturing Forecasting Uncertainty Digital Transformation 52

The Unofficial Google Data Science Blog

APRIL 17, 2017

Selection and aggregation of forecasts from an ensemble of models to produce a final forecast. Quantification of forecast uncertainty via simulation-based prediction intervals. We conclude with an example of our forecasting routine applied to publicly available Turkish Electricity data.

Forecasting 83

Forecasting Modeling Statistics Uncertainty 83

Peter James Thomas

AUGUST 12, 2018

This piece was prompted by both Olaf’s question and a recent article by my friend Neil Raden on his Silicon Angle blog, Performance management: Can you really manage what you measure? Data may be perfectly valid, but still not represent reality. It is hard to account for such tweaking in measurement systems.

Metrics 49

Metrics Statistics Data Quality Measurement 49

The Unofficial Google Data Science Blog

MARCH 13, 2017

By MUKUND SUNDARARAJAN, ANKUR TALY, QIQI YAN Editor's note: Causal inference is central to answering questions in science, engineering and business and hence the topic has received particular attention on this blog. We are all familiar with linear and logistic regression models. The top few features with the largest weight (i.e.,

IT 68

IT Visualization Modeling Uncertainty 68

The Unofficial Google Data Science Blog

APRIL 16, 2019

Modern products often don't start with set-in-stone business models because starting and scaling costs are low. The metrics to measure the impact of the change might not yet be established. It can provide a post hoc measure of eventual impact, and hence insight into what the product might try next. Do you even want those users?

Experimentation 68

Experimentation Sales Metrics Measurement 68

The Unofficial Google Data Science Blog

MAY 31, 2016

It is important that we can measure the effect of these offline conversions as well. Panel studies make it possible to measure user behavior along with the exposure to ads and other online elements. Wouldn't it be great if we didn't require individual data to estimate an aggregate effect? days or weeks).

Advertising 42

Advertising Testing Sales Statistics 42

The Unofficial Google Data Science Blog

AUGUST 27, 2019

The measurement may be biased if our samples are generated from a procedure that samples without replacement, such as reservoir sampling , especially if some items have disproportionate weight, i.e., $p(v_i) cdot n$ is large. An Introduction to Model-Based Survey Sampling with Applications. [6] High Risk 10% 5% 33.3% How Many Strata?

Metrics 98

Metrics Statistics Uncertainty Optimization 98

O'Reilly on Data

OCTOBER 13, 2020

Similarly, in “ Building Machine Learning Powered Applications: Going from Idea to Product ,” Emmanuel Ameisen states: “Indeed, exposing a model to users in production comes with a set of challenges that mirrors the ones that come with debugging a model.”.

Management 244

Management Metrics Machine Learning Modeling 244

The Unofficial Google Data Science Blog

OCTOBER 21, 2015

Our post describes how we arrived at recent changes to design principles for the Google search page, and thus highlights aspects of a data scientist’s role which involve practicing the scientific method. There has been debate as to whether the term “data science” is necessary. Some don’t see the point.

Slice and Dice 45

Slice and Dice Experimentation Data-driven Data Science 45

The Unofficial Google Data Science Blog

FEBRUARY 29, 2016

The result is that experimenters can’t afford to be sloppy about quantifying uncertainty. These typically result in smaller estimation uncertainty and tighter interval estimates. And since the metric average is different in each hour of day, this is a source of variation in measuring the experimental effect.

Experimentation 40

Experimentation Metrics Statistics Measurement 40