Domino Data Lab

MAY 20, 2021

Working with highly imbalanced data can be problematic in several aspects: Distorted performance metrics — In a highly imbalanced dataset, say a binary dataset with a class ratio of 98:2, an algorithm that always predicts the majority class and completely ignores the minority class will still be 98% correct. return synthetic. Chawla et al.,

Machine Learning 136

Machine Learning Metrics Data mining Knowledge Discovery 136

CIO Business Intelligence

MAY 27, 2024

According to the Ponemon Institute’s 2023 Cost of Data Breach Report , organizations with extensive incident response planning and testing programs saved $1.49 Speak the Board’s Language Board members are often more attuned to financial metrics and business outcomes than technical jargon. million compared to those with lower levels.

Cost-Benefit 116

Cost-Benefit Statistics ROI Measurement 116

AWS Big Data

AUGUST 9, 2024

Amazon Redshift at Dafiti Amazon Redshift is a fully managed data warehouse service, and was adopted by Dafiti in 2017. AWS offers Redshift Test Drive to validate whether the configuration chosen for Amazon Redshift is ideal for your workload before migrating the production environment. We started with 115 dc2.large

Data Lake 107

Data Lake Analytics Data Warehouse Data-driven 107

Occam's Razor

DECEMBER 8, 2017

If the Surface Marketing team is like every other team at every other company engaged in sponsorships and television advertising, it’ll measure the same collection of smart metrics like everyone else. Better than Reach and Brand Lift metrics? As the season went on, we could look for test and control opportunities.

Broadcasting 123

Broadcasting Measurement Sales Marketing 123

Data Science 101

MAY 6, 2019

This was released as MBUX in 2017 and that was a pretty cool project because there were a lot of challenges that you wouldn’t normally have to solve if you had access to a massive data set or connectivity. We pulled questions from some of our Springboard students and this one is from Miguel, who is in our data science vertical.

Data Science 98

Data Science Modeling Machine Learning Testing 98

CIO Business Intelligence

MAY 16, 2023

higher [in 2022] than in 2017.” Trust and Safety: Individuals and organizations critical of social media are turning their attention to the metaverse, scrutinizing and testing systems intended to promote privacy and child safety and protect against hate speech and harassment.

Advertising 105

Advertising Recreation/Entertainment Experimentation Data Collection 105

IBM Big Data Hub

FEBRUARY 8, 2024

It refers to a set of metrics used to measure an organization’s environmental and social impact and has become increasingly important in investment decision-making over the years. In response, asset managers began to develop ESG strategies and metrics to measure the environmental and social impact of their investments.

Measurement 77

Measurement Metrics Reporting Strategy 77

Sisense

JULY 10, 2019

As of 2017, Linux was running 90% of the public cloud workload. Given this importance, we made it an organizational priority to invest in a Sisense on Linux deployment in late 2017. When I was tasked with the responsibility of building a Sisense Linux deployment in late 2017, a few small steps had already been taken.

IT 69

IT Testing Technology Sales 69

Insight

MARCH 25, 2019

Bugs in a third-party CDN provider’s system, such as this one from 2017, can have serious implications for the security and privacy of your users. I incorporated ExternalDNS into kubeCDN and configured my video test service to use ExternalDNS and set a DNS record when deployed.

Data Processing 106

Data Processing Cost-Benefit Optimization Metrics 106

The Unofficial Google Data Science Blog

NOVEMBER 17, 2020

For example, consider the following simple example fitting a two-dimensional function to predict if someone will pass the bar exam based just on their GPA (grades) and LSAT (a standardized test) using the public dataset (Wightman, 1998). Curiosities and anomalies in your training and testing data become genuine and sustained loss patterns.

Machine Learning 111

Machine Learning Deep Learning Modeling Interactive 111

Cloudera

MAY 18, 2021

Value-at-Risk (VaR) is a widely used metric in risk management. It helps identify risk exposures, informs pre-trade decisions, and is reported to regulators for stress testing. 2] Bayraktar, Erhan and Munk, Alexander, Mini-Flash Crashes, Model Risk, and Optimal Execution (May 27, 2017). Intraday VaR. Citations. [1]

Risk 99

Risk Marketing Risk Management Data-driven 99

Insight

OCTOBER 25, 2019

This category was not considered for the purpose of this project as it does not allow for a 3-way partition for disjoint training, validation, and testing sets. My client also specified that CAD model files of the T-LESS dataset be used for this project, and that one object per class be reserved for testing (Objects 4, 8, 12, 18, 23, 30).

Snapshot 57

Snapshot Testing Deep Learning Visualization 57

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., Experiments, Parameters and Models At Youtube, the relationships between system parameters and metrics often seem simple — straight-line models sometimes fit our data well.

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

Alation

AUGUST 26, 2021

HBR Review May/June 2017. A common BI application is the BI dashboard, which displays key metrics so that leaders have a “big picture view” to inform wise goals and decisions. Next, you test these use cases with the software chosen. Field Test Use Cases. Source: “What’s Your Data Strategy?” Data Intelligence and Metadata.

Metadata 64

Metadata Data Governance Dashboards Software 64

Smarten

NOVEMBER 22, 2017

These tools allow the enterprise to establish and monitor key metrics and to objectively assess results. With the power to test competitive and market strategies, management and business users can see and leverage the hidden value within the organization and find the small problems before they become large issues.

Analytics 40

Analytics IT Business Intelligence Visualization 40

Depict Data Studio

SEPTEMBER 13, 2023

Grant Deliverables In this blog post , you’ll see how Josephine Engels did need to start from scratch — she was visualizing these metrics for her organization for the first time — and then made several dashboards to track grant deliverables. Adapt one of these dashboards instead. Learn how inside Dashboard Design.

Dashboards 52

Dashboards Visualization Snapshot Data-driven 52

Domino Data Lab

OCTOBER 23, 2019

Knowing that the ultimate goal is to compare the social-media influence and power of NBA players, a great place to start is with the roster of the NBA players in the 2016–2017 season. You can add a Makefile command test that will run all of your notebooks by issuing. test: py.test --nbval notebooks/*.ipynb. 35,vjust=1).

Statistics 59

Statistics Machine Learning Testing Modeling 59

Occam's Razor

SEPTEMBER 15, 2014

They will need two different implementations, it is quite likely that you will end up with two sets of metrics (more people focused for mobile apps, more visit focused for sites). Mobile content consumption, behavior along key metrics (time, bounces etc.) If you have ecommerce you will see key metrics related to money making.

Metrics 143

Metrics Reporting Analytics Marketing 143

Domino Data Lab

MAY 8, 2019

Finale Doshi-Velez, Been Kim (2017-02-28) ; see also the Domino blog article about TCAV. Adrian Weller (2017-07-29). “ If your “performance” metrics are focused on predictive power, then you’ll probably end up with more complex models, and consequently less interpretable ones. Challenges for Transparency ”. 2018-06-21).

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

Domino Data Lab

AUGUST 1, 2021

Because of its architecture, intrinsically explainable ANNs can be optimised not just on its prediction performance, but also on its explainability metric. After forming the X and y variables, we split the data into training and test sets. For sample 23 from the test set, the model is leaning towards a bad credit prediction.

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139

CIO Business Intelligence

JULY 20, 2022

Yet in the cybersecurity field, women only account for about one quarter (24%) of the overall workforce, albeit, up from 11% in 2017, according to the ISC2 Women in Cybersecurity report. We are testing and learning our way into what to do differently both to attract and retain a more diverse set of talent and help them thrive and grow.”.

IT 136

IT Software Cost-Benefit Strategy 136

Occam's Razor

JANUARY 8, 2018

If you are a reader of my newsletter, The Marketing < > Analytics Intersect , you’ve seen me apply it to metrics (last TMAI was on Bounce Rate), reports, frameworks and more. In 2017, they've twice replaced the company's Chief Revenue Officer. Had it been tested? Condé Nast | A Story of Unrequited Love.

Sales 135

Sales Predictive Analytics Insurance Data Warehouse 135

The Unofficial Google Data Science Blog

JULY 18, 2023

Once we’ve answered that, we will then define and use metrics to understand the quality of human-labeled data, along with a measurement framework that we call Cross-replication Reliability or xRR. We will follow the example of Janson and Olsson , and start from this generalized definition of the metric, which they call iota.

Measurement 52

Measurement Metrics Uncertainty Slice and Dice 52

O'Reilly on Data

JANUARY 25, 2021

Both SRE and DevOps emphasize similar practices: version control (62% growth for GitHub, and 48% for Git), testing (high usage, though no year-over-year growth), continuous deployment (down 20%), monitoring (up 9%), and observability (up 128%). It’s particularly difficult if testing includes issues like fairness and bias.

Machine Learning 364

Machine Learning Software Testing Technology 364

AWS Big Data

NOVEMBER 13, 2024

The first use case helps predict test results during the car assembly process. The second use case enables the creation of reports containing shop floor key metrics for different management levels. This growth is measured by metrics such as number of data products, number of use cases onboarded into the solution, and number of users.

Metadata 94

Metadata Data Quality Digital Transformation Data-driven 94