Domino Data Lab

MAY 8, 2019

Paco Nathan’s latest article features several emerging threads adjacent to model interpretability. I’ve been out themespotting and this month’s article features several emerging threads adjacent to the interpretability of machine learning models. Machine learning model interpretability. Adrian Weller (2017-07-29). “

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

The Unofficial Google Data Science Blog

NOVEMBER 17, 2020

by TAMAN NARAYAN & SEN ZHAO A data scientist is often in possession of domain knowledge which she cannot easily apply to the structure of the model. On the one hand, basic statistical models (e.g. On the other hand, sophisticated machine learning models are flexible in their form but not easy to control.

Machine Learning 108

Machine Learning Deep Learning Modeling Interactive 108

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. def get_neigbours(M, k): nn = NearestNeighbors(n_neighbors=k+1, metric="euclidean").fit(M) Figure 3 shows visual explanation of how SMOTE generates synthetic observations in this case.

Machine Learning 136

Machine Learning Metrics Data mining Knowledge Discovery 136

Smarten

JANUARY 7, 2020

Every business understands the value of objective metrics and accurate analysis and the modern BI environment is designed to support these goals at every level of the organization. How is Modern BI Different From Traditional BI? Modern BI supports collaboration, while providing appropriate data governance and data security.

IT 52

IT Business Intelligence ROI Reporting 52

Domino Data Lab

FEBRUARY 4, 2019

People who attended JupyterCon 2017–2018 can attest, an “industry poster session” includes an open bar, catered hors d’oeuvres, lots of mingling … to paraphrase feedback from JupyterCon, “As a tech person, would I get up extra early to meet strangers for coffee at 8:00 am? I double-dare you not to visualize that cohort!

Data Science 66

Data Science Experimentation Data-driven Machine Learning 66

Domino Data Lab

OCTOBER 23, 2019

This chapter will explore the numbers behind the numbers using ML and then creating an API to serve out the ML model. This means covering details like setting up your environment, deployment, and monitoring, in addition to creating models on clean data. Phrasing the Problem. questions to ask. Gathering the Data. In Figure 6.1,

Statistics 59

Statistics Machine Learning Testing Modeling 59

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

datapine

NOVEMBER 27, 2019

Spreadsheets finally took a backseat to actionable and insightful data visualizations and interactive business dashboards. Companies are no longer wondering if data visualizations improve analyses but what is the best way to tell each data-story. 2) Data Discovery/Visualization. Data exploded and became big.

Business Intelligence 361

Business Intelligence Analytics Prescriptive Analytics Data Quality 361

Alation

AUGUST 26, 2021

HBR Review May/June 2017. These future-oriented models are used to make predictions. BI leverages and synthesizes data from analytics, data mining, and visualization tools to deliver quick snapshots of business health to key stakeholders, and empower those people to make better choices. Source: “What’s Your Data Strategy?”

Metadata 64

Metadata Data Governance Dashboards Software 64

Occam's Razor

JULY 13, 2017

In service of that belief, there are few things that bring me as much joy as visualizing data (smart segmentation comes close). While I am partial to the simplest of visualizations in a business data context, I love a simple Bar Chart just as much as a Chord or Fisher-Yates Shuffle. 5: What-if Analysis Models.

Visualization 124

Visualization Strategy Interactive Statistics 124

Domino Data Lab

SEPTEMBER 9, 2019

We have configured the default Compute Environment in Domino to include all of the packages, libraries, models, and data you’ll need for this tutorial. That nlp variable is now your gateway to all things spaCy and loaded with the en_core_web_sm small model for English. Getting Started. lemma – a root form of the word.

Deep Learning 110

Deep Learning Machine Learning Data Science Visualization 110