Domino Data Lab

MAY 20, 2021

The problem with this approach is that in highly imbalanced sets it can easily lead to a situation where most of the data has to be discarded, and it has been firmly established that when it comes to machine learning data should not be easily thrown out (Banko and Brill, 2001; Halevy et al., Chawla et al. References. link] Chawla, N.

Machine Learning 136

Machine Learning Metrics Data mining Knowledge Discovery 136

Domino Data Lab

APRIL 3, 2019

Also, while surveying the literature two key drivers stood out: Risk management is the thin-edge-of-the-wedge ?for My read of that narrative arc is that some truly weird tensions showed up circa 2001: Arguably, it’s the heyday of DW+BI. A very big mess since circa 2001, and now becoming quite a dangerous mess. a second priority?at

Data Governance 84

Data Governance Machine Learning Metadata Big Data 84

Domino Data Lab

AUGUST 8, 2019

Consider the following timeline: 2001 – Physics grad students are getting hired in quantity by hedge funds to work on Wall St. The probabilistic nature changes the risks and process required. We face problems—crises—regarding risks involved with data and machine learning in production. Public Health Reports (2017-07-10).

Data Science 71

Data Science Machine Learning Data Governance Statistics 71

Domino Data Lab

JANUARY 6, 2019

What are the projected risks for companies that fall behind for internal training in data science? In terms of teaching and learning data science, Project Jupyter is probably the biggest news over the past decade – even though Jupyter’s origins go back to 2001! Wes McKinney (2017). Aurélien Géron (2017).

Data Science 56

Data Science Machine Learning Reporting Visualization 56