DataRobot

APRIL 5, 2021

The uncertainty in her reply piqued my interest. This isn’t a criticism of CIOs, but rather a reminder that AI projects require a broader frame of reference than conventional IT projects. A few years ago at a trade show, I asked a business manager who came to our booth if her employer hired any data scientists.

Uncertainty 96

Uncertainty Machine Learning Modeling Reporting 96

The Unofficial Google Data Science Blog

JULY 18, 2023

E ven after we account for disagreement, human ratings may not measure exactly what we want to measure. Researchers and practitioners have been using human-labeled data for many years, trying to understand all sorts of abstract concepts that we could not measure otherwise. That’s the focus of this blog post.

Measurement 52

Measurement Metrics Uncertainty Slice and Dice 52

The Unofficial Google Data Science Blog

JANUARY 14, 2016

Unlike experimentation in some other areas, LSOS experiments present a surprising challenge to statisticians — even though we operate in the realm of “big data”, the statistical uncertainty in our experiments can be substantial. We must therefore maintain statistical rigor in quantifying experimental uncertainty. An effect size of 0.2

Experimentation 41

Experimentation Statistics Metrics Measurement 41

IBM Big Data Hub

APRIL 18, 2024

AGI, sometimes referred to as strong AI , is the science-fiction version of artificial intelligence (AI), where artificial machine intelligence achieves human-level learning, perception and cognitive flexibility. The AGI would need to handle uncertainty and make decisions with incomplete information.

Cost-Benefit 89

Cost-Benefit Modeling Manufacturing Interactive 89

The Unofficial Google Data Science Blog

APRIL 23, 2024

the weight given to Likes in our video recommendation algorithm) while $Y$ is a vector of outcome measures such as different metrics of user experience (e.g., Crucially, it takes into account the uncertainty inherent in our experiments. Figure 2: Spreading measurements out makes estimates of model (slope of line) more accurate.

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

Occam's Razor

APRIL 8, 2013

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. For each of them, write down the KPI you're measuring, and what that KPI should be for you to consider your efforts a success. Measure and decide what to do.

Metrics 156

Metrics KPI Analytics Key Performance Indicator 156

DataKitchen

SEPTEMBER 21, 2023

The uncertainty of not knowing where data issues will crop up next and the tiresome game of ‘who’s to blame’ when pinpointing the failure. Data in Place refers to the organized structuring and storage of data within a specific storage medium, be it a database, bucket store, files, or other storage platforms.

Testing 169

Testing Data Quality Predictive Modeling Metrics 169

The Unofficial Google Data Science Blog

APRIL 17, 2017

Quantification of forecast uncertainty via simulation-based prediction intervals. First, the system may not be understood, and even if it was understood it may be extremely difficult to measure the relationships that are assumed to govern its behavior. Crucially, our approach does not rely on model performance on holdout samples.

Forecasting 83

Forecasting Modeling Statistics Uncertainty 83

Occam's Razor

JULY 12, 2010

Let's go look at some tools… Measuring "Invisible Virality": Tynt. It measures how often a blog post is tweeted/retweeted. I also measure the # of Comments Per Post as a measure of how "engaging" / "valuable" people found the content to be. Or for that matter how many tools.

Analytics 96

Analytics Measurement Metrics KPI 96

Data Science and Beyond

JULY 28, 2017

While some people may find this kind of uncertainty daunting, I find it interesting, as it is one of the things that makes data science a science. It tells you something you can’t learn from resumes, interviews, or reference checks. If you’re a designer, you’ll design. And after 2.5 moving away from Legacy Python ).

Data Science 81

Data Science Testing Measurement Uncertainty 81

CIO Business Intelligence

APRIL 28, 2024

However, new energy is restricted by weather and climate, which means extreme weather conditions and unpredictable external environments bring an element of uncertainty to new energy sources. It was the solution of choice to achieve an observable, measurable, adjustable, controllable and traceable low-voltage side. HPLC can deliver 99.9%

Interactive 68

Interactive Uncertainty Data Collection IoT 68

The Unofficial Google Data Science Blog

JULY 22, 2020

This post considers a common design for an OCE where a user may be randomly assigned an arm on their first visit during the experiment, with assignment weights referring to the proportion that are randomly assigned to each arm. For this reason we don’t report uncertainty measures or statistical significance in the results of the simulation.

Experimentation 95

Experimentation Statistics Testing Strategy 95

The Unofficial Google Data Science Blog

AUGUST 27, 2019

We refer interested readers to [1] (cited here with the author's permission) on the details of importance sampling, and will follow the notation from [1] in what follows. However, in the case of binomial proportions far from $frac{1}{2}$, estimates of $hat{mu}_q$ and $hat{mu}_{q, PS}$ on finite samples converge poorly to the true uncertainty.

Metrics 98

Metrics Statistics Uncertainty Optimization 98

O'Reilly on Data

JULY 20, 2021

Contextual informational norms refer to five independent parameters: data subject, sender, recipient, information type, and transmission principle. This measurement of trust and risk is benefited by understanding who could be in front of the device. Appropriate information flows are those that conform to contextual information norms.

Risk 308

Risk Software Interactive Modeling 308

The Unofficial Google Data Science Blog

DECEMBER 4, 2019

This classification is based on the purpose, horizon, update frequency and uncertainty of the forecast. A single model may also not shed light on the uncertainty range we actually face. These characteristics of the problem drive the forecasting approaches.

Forecasting 113

Forecasting Data Science Statistics Uncertainty 113

Domino Data Lab

JUNE 2, 2019

And by “scale” I’m referring to what is arguably the largest, most successful data analytics operation in the cloud of any public firm that isn’t a cloud provider. Clearly, when we work with data and machine learning, we’re swimming in those waters of decision-making under uncertainty.

Data-driven 110

Data-driven Data Science Machine Learning Modeling 110

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. For quick reference, here’s a summary of the advice in this post: Use an accurate method for estimating confidence intervals. Use enough resamples – at least 10-15K.

Statistics 77

Statistics Uncertainty Testing Modeling 77

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. Let's take a look at larger groups of individuals whose aggregate behavior we can measure. days or weeks).

Advertising 42

Advertising Testing Sales Statistics 42

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? It is hard to account for such tweaking in measurement systems. Some relate to inherent issues with what is being measured.

Metrics 49

Metrics Statistics Data Quality Measurement 49

O'Reilly on Data

DECEMBER 9, 2019

Because of this trifecta of errors, we need dynamic models that quantify the uncertainty inherent in our financial estimates and predictions. Practitioners in all social sciences, especially financial economics, use confidence intervals to quantify the uncertainty in their estimates and predictions.

Statistics 27

Statistics Uncertainty Risk Marketing 27

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. At Google, we tend to refer to them as slices. Let’s go back to our example of measuring the fraction of user sessions with purchase.

Experimentation 40

Experimentation Metrics Statistics Measurement 40

The Unofficial Google Data Science Blog

MARCH 13, 2017

Typically, causal inference in data science is framed in probabilistic terms, where there is statistical uncertainty in the outcomes as well as model uncertainty about the true causal mechanism connecting inputs and outputs. References [1] Szegedy, Christian, Liu, Wei, Jia, Yangqing, Sermanet, Pierre, Reed, Scott E.,

IT 68

IT Visualization Modeling Uncertainty 68

datapine

DECEMBER 28, 2021

With the rise of advanced technology and globalized operations, statistical analyses grant businesses an insight into solving the extreme uncertainties of the market. Misleading statistics refers to the misuse of numerical data either intentionally or by error. In 2012, the global mean temperature was measured at 58.2

Statistics 363

Statistics Advertising Visualization Data mining 363

Jen Stirrup

NOVEMBER 15, 2022

The RPA market may grow to $25 billion in 2025 according to Forrester, and it has the promise of supporting digital transformation through streamlining digital transformation ( Reference ). The foundation should be well structured and have essential data quality measures, monitoring and good data engineering practices.

Data Science 145

Data Science Digital Transformation Data-driven Measurement 145

Jet Global

JULY 14, 2022

The accrual basis of accounting measures the financial position and performance of your business by understanding economic events regardless of when cash transactions occur. You may hear billing software referred to as invoicing software. It’s a measure of negative cash flow. Accrual Basis of Accounting. Bottom Line.

Finance 52

Finance Cost-Benefit Measurement Sales 52

Jet Global

OCTOBER 4, 2021

Management gurus have long been advocates of measuring, monitoring, and reporting on the numbers that matter most. You measure it using three common financial metrics, namely, days of inventory (DOI), days of payables (DOP), and days sales outstanding (DSO). Add DOI and DOP, then subtract DSO to arrive at cash to cash cycle time.

Management 96

Management KPI Metrics Recreation/Entertainment 96

O'Reilly on Data

JUNE 16, 2020

But when making a decision under uncertainty about the future, two things dictate the outcome: (1) the quality of the decision and (2) chance. This essay is about how to take a more principled approach to making decisions under uncertainty and aims to provide certain conceptual and cognitive tools for how to do so, not what decisions to make.

Uncertainty 282

Uncertainty Testing Risk Reporting 282