Domino Data Lab

DECEMBER 4, 2020

NCA doesn’t require the assumption of a specific compartmental model for either drug or metabolite; it is instead assumption-free and therefore easily automated [1]. PharmaceUtical Modeling And Simulation (or PUMAS) is a suite of tools to perform quantitative analytics for pharmaceutical drug development [2]. Mean residence time.

Metrics 59

Metrics Data Science Knowledge Discovery Measurement 59

Ontotext

SEPTEMBER 15, 2020

There must be a representation of the low-level technical and operational metadata as well as the ‘real world’ metadata of the business model or ontologies. These additional software components need to be updated, tested and deployed, which goes counter to the Data Fabric goal of creating frictionless movement of data.

Metadata 69

Metadata Knowledge Discovery Data Quality Strategy 69

Ontotext

AUGUST 16, 2019

The training is structured to follow the steps of building a simple prototype to test the feasibility of the technology with hands-on guidance by experienced instructors. The most important question our training tries to answer, both in theory and in practice, is how to approach a use case that is a good fit for semantic technology.

Knowledge Discovery 40

Knowledge Discovery Technology Metadata Interactive 40

The Unofficial Google Data Science Blog

NOVEMBER 4, 2015

But most common machine learning methods don’t give posteriors, and many don’t have explicit probability models. More precisely, our model is that $theta$ is drawn from a prior that depends on $t$, then $y$ comes from some known parametric family $f_theta$. Here, our items are query-ad pairs. Calculate posterior quantities of interest.

KDD 40

KDD Testing Machine Learning Measurement 40

Domino Data Lab

MAY 20, 2021

In this article we discuss why fitting models on imbalanced datasets is problematic, and how class imbalance is typically addressed. Their tests are performed using C4.5-generated note that this variant “performs worse than plain under-sampling based on AUC” when tested on the Adult dataset (Dua & Graff, 2017). Chawla et al.,

Machine Learning 136

Machine Learning Metrics Data mining Knowledge Discovery 136

The Unofficial Google Data Science Blog

JANUARY 14, 2016

Of particular interest to LSOS data scientists are modeling and prediction techniques which keep improving with more data. For this purpose, let’s assume we use a t-test for difference between group means. To observe this, let $W$ be the sample average differences between groups (our test statistic).

Experimentation 41

Experimentation Statistics Metrics Measurement 41

Ontotext

APRIL 4, 2019

Milena Yankova : Our work is focused on helping companies make sense of their own knowledge. Within a large enterprise, there is a huge amount of data accumulated over the years – many decisions have been made and different methods have been tested. Some of this knowledge is locked and the company cannot access it.

Recreation/Entertainment 76

Recreation/Entertainment Testing Enterprise Knowledge Discovery 76

Domino Data Lab

AUGUST 1, 2021

In this article we cover explainability for black-box models and show how to use different methods from the Skater framework to provide insights into the inner workings of a simple credit scoring neural network model. The interest in interpretation of machine learning has been rapidly accelerating in the last decade. See Ribeiro et al.

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139

Ontotext

AUGUST 21, 2024

There is a confluence of activity—including generative AI models, digital twins, and shared ledger capabilities—that are having a profound impact on helping enterprises meet their goal of becoming data driven. Equally important, it simplifies and automates the governance operating model.

Knowledge Discovery 52

Knowledge Discovery Cost-Benefit Data-driven Metadata 52