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., The unreasonable effectiveness of data.

Machine Learning 136

Machine Learning Metrics Data mining Knowledge Discovery 136

Peter James Thomas

APRIL 9, 2019

This increase was driven in part by the launch of my new Maths & Science section , articles from which claimed no fewer than 6 slots in the 2018 top 10 articles, when measured by hits [1]. Given the advent of the Maths & Science section, there are now seven categories into which I have split articles. Data Visualisation.

Data-driven 49

Data-driven Statistics Big Data Data Science 49

Data Science and Beyond

MARCH 29, 2015

I started in March 2009 and submitted my thesis in August 2012. I did my PhD at Monash University under the supervision of Ingrid Zukerman and Fabian Bohnert.

IT 52

IT 52

Domino Data Lab

JUNE 8, 2021

The intuition here is that a decision boundary that leaves a wider margin between the classes generalises better, which leads us to the key property of support vector machines — they construct a hyperplane in a such a way that the margin of separation between the two classes is maximised (Haykin, 2009). Derivation of a Linear SVM. Fisher, R.

Optimization 76

Optimization Machine Learning Measurement Data Science 76

DataRobot

JUNE 17, 2021

Distilling Free-Form Natural Laws from Experimental Data, Science 03 Apr 2009: Vol. 81-85, Paper Link Silviu-Marian Udrescu, Max Tegmark “AI Feynman: A physics-inspired method for symbolic regression,” Science Advances 15 Apr 2020: Vol. You can also find a walkthrough guide on our community page. References. Schmidt, M.,

Modeling 52

Modeling Experimentation Machine Learning Measurement 52

Domino Data Lab

MAY 8, 2019

The lens of reductionism and an overemphasis on engineering becomes an Achilles heel for data science work. Instead, consider a “full stack” tracing from the point of data collection all the way out through inference. back to the structure of the dataset. Let’s look through some antidotes. Ergo, less interpretable.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

datapine

JANUARY 31, 2022

Although the oil company has been producing massive amounts of data for a long time, with the rise of new cloud-based technologies and data becoming more and more relevant in business contexts, they needed a way to manage their information at an enterprise level and keep up with the new skills in the data industry.

Business Intelligence 214

Business Intelligence Analytics Cost-Benefit ROI 214

Sisense

JANUARY 21, 2020

To better understand when the data should be grouped, you should be familiar with causal inference. If you don’t have the time to read “The Book of Why,’” you can refer to Towards Data Science. In 2009, researchers suggested that Simpson’s paradox may occur more often than commonly thought.

Testing 104

Testing Risk Data-driven Statistics 104

bridgei2i

APRIL 16, 2021

He has over a decade of experience in the field of AI, which he started with his paper on machine learning in 2009. So, I remember when I was studying, and there were a lot of buzzwords like big data, Hadoop making the rounds and then came data science. His journey to the field of AI is an interesting one.

Deep Learning 98

Deep Learning Machine Learning Digital Transformation Software 98

Peter James Thomas

JUNE 20, 2019

The above chart compares monthly searches for Business Process Reengineering (including its arguable rebranding as Business Transformation ) and monthly searches for Data Science between 2004 and 2019. Here we come back to the upward trend in searches for Data Science. – McKinsey 2009. . [6].

Cost-Benefit 55

Cost-Benefit Data Warehouse Consulting Data Science 55

The Unofficial Google Data Science Blog

JULY 11, 2017

SCOTT Time series data are everywhere, but time series modeling is a fairly specialized area within statistics and data science. Introduction Time series data appear in a surprising number of applications, ranging from business, to the physical and social sciences, to health, medicine, and engineering. Petrone, S.,

Forecasting 64

Forecasting Statistics Modeling Software 64

Jen Stirrup

APRIL 2, 2019

So, we made efforts to have real chops and apply our research and findings to our own organization, and to really analyze the data, and to apply marketing theory and practice to improving , informed by the academic literature. Data can help us to understand the research to explore and elicit reasons for avoidance of brands (Lee et al.,

Marketing 59

Marketing Advertising Data-driven Strategy 59

Peter James Thomas

AUGUST 12, 2018

Especially for all Business Analytics professionals out there (2009). [7]. See in particular my trilogy: Using historical data to justify BI investments – Part I (2011). Using historical data to justify BI investments – Part II (2011). Using historical data to justify BI investments – Part III (2011). [8].

Metrics 49

Metrics Statistics Data Quality Measurement 49

Domino Data Lab

APRIL 21, 2021

The complete dataset and code used in this blog post is available at try.dominodatalab.com, and all results shown here are fully reproducible, thanks to the Domino reproducibility engine, which is part of the Domino Data Science platform. Learning from Imbalanced Data. References. [1] Pavía, Ernesto J. 2] Nitesh V. 3] He, H. &

Statistics 69

Statistics Machine Learning Modeling Metrics 69

The Unofficial Google Data Science Blog

MARCH 13, 2017

By MUKUND SUNDARARAJAN, ANKUR TALY, QIQI YAN Editor's note: Causal inference is central to answering questions in science, engineering and business and hence the topic has received particular attention on this blog. 2009, " Measuring invariances in deep networks ". Technical Report 1341, University of Montreal, 2009.

IT 68

IT Visualization Modeling Uncertainty 68

The Unofficial Google Data Science Blog

NOVEMBER 17, 2020

GAMs are popular among data science and machine learning applications for their simplicity and interpretability. For example, one could apply logarithms or add polynomial terms for certain features. A more general approach is to learn a Generalized Additive Model (GAM). References Wightman, L. Law School Admission Council, 1998.

Machine Learning 108

Machine Learning Deep Learning Modeling Interactive 108