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

Smart Data Collective

MARCH 13, 2020

Phase 4: Knowledge Discovery. Finally, models are developed to explain the data. Algorithms can also be tested to come up with ideal outcomes and possibilities. When these two elements are in harmony, there are fewer delays and less risk of data corruption. Make the Workflow Obvious and Apparent to Others.

Data Science 100

Data Science Key Performance Indicator Knowledge Discovery Visualization 100

The Unofficial Google Data Science Blog

JULY 22, 2020

One reason to do ramp-up is to mitigate the risk of never before seen arms. A ramp-up strategy may mitigate the risk of upsetting the site’s loyal users who perhaps have strong preferences for the current statistics that are shown. For example, imagine a fantasy football site is considering displaying advanced player statistics.

Experimentation 95

Experimentation Statistics Testing Strategy 95

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 This carries the risk of this modification performing worse than simpler approaches like majority under-sampling. Chawla et al., 1998) and others).

Machine Learning 136

Machine Learning Metrics Data mining Knowledge Discovery 136

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 Modeling 40

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