Statistics Archives - Open Data Science - Your News Source for AI, Machine Learning & more

Why Do Tree Ensembles Work?

Guest contributor Machine Learning Modeling Statistics Machine Learning Statisticsposted by Joe Ross April 10, 2019

Ensembles of decision trees (e.g., the random forest and AdaBoost algorithms) are powerful and well-known methods of classification and regression. We will survey work aimed at understanding the statistical properties of decision tree ensembles, with the goal of explaining why they work. An elementary probabilistic motivation for ensemble methods... Read more

The Importance of P-Values in Data Science

Modeling Statisticsposted by Daniel Gutierrez, ODSC February 26, 2019

The field of data science makes use of concepts from a variety of disciplines, particularly computer science, mathematics, and applied statistics. One term that keeps popping up in data science circles (including many interviews for data scientist employment positions) is “p-value” which comes from statistics. This term is frequently... Read more

Confidence Intervals for Data Scientists

Modeling Statistics Statisticsposted by Daniel Gutierrez, ODSC January 17, 2019

Confidence interval is a basic statistical concept commonly employed by data scientists. Without a formal background in statistics, however, some data scientists tend to scratch their heads with respect to their understanding of what’s really going on with this notion. In this article, we’ll review the basics of confidence... Read more

How to Play Fantasy Sports Strategically (and Win)

Statistics Conferences Dirichlet Distribution Fantasy Football Martin Haugh ODSC Europe Statisticsposted by Paxtyn Merten November 30, 2018

Daily Fantasy Sports is a multibillion-dollar industry with millions of annual users. The Imperial College Business School’s Martin Haugh created a framework to best those users by modeling what they’ll do and constructing a team based on it. Haugh presented his research on how to play Fantasy sports strategically... Read more

Thomas Wiecki of Quantopian on ‘Minding the Gap’ Between Statistics and Machine Learning at ODSC Europe 2018

Conferences Modeling Statistics Conferences Machine Learning ODSC Europe Statisticsposted by Daniel Gutierrez, ODSC November 20, 2018

Key Takeaways: It’s important for data scientists to understand the so-called “gap” between statistics and machine learning, and how there actually is a lot of commonality between the two; it’s just a matter of how you look at things. PyMC3 is a very useful probabilistic programming framework for Python.... Read more

Exploring the Central Limit Theorem in R

Statistics Central Limit Theorem Statisticsposted by Daniel Gutierrez, ODSC November 9, 2018

The Central Limit Theorem (CLT) is arguably the most important theorem in statistics. It’s certainly a concept that every data scientist should fully understand. In this article, we’ll go over some basic theory of the CLT, explain why it’s important for data scientists, and present some R code that... Read more

Mine Like Amazon with Market Basket Analysis

Modeling Statistics market basket analysis Statisticsposted by Spencer Norris, ODSC October 12, 2018

Pattern mining is an incredibly simple but powerful technique for discovering cooccurrences in large datasets. The most common approach to find those patterns is Market Basket Analysis, which is frequently pointed out as the method Amazon leverages for their “users also purchased” feature. Of course, that’s a dramatic oversimplification.... Read more

Building a Microservice for Twitter Real-Time Data Collection and Sentiment Analysis.

Modeling Statisticsposted by Alexander Osipenko September 21, 2018

First of all, I would like to point out that the skill of building MVP and microservices for a data scientist is extremely useful! When you can build a prototype and test it in a working environment it just feels so much better and allows you to better understand... Read more

Intro to Ontologies

Modeling Statisticsposted by Spencer Norris, ODSC September 10, 2018

Statistical methods are inarguably the hottest approach to evaluating datasets at scale right now. They’re not without their weaknesses though – they’re ultimately heuristic, and some methods like neural networks require tremendous amounts of data to create a well-fitted model. That’s where semantics come in. If statistical methods attempt... Read more

Tips for Linear Regression Diagnostics

Modeling Statisticsposted by Daniel Gutierrez, ODSC August 29, 2018

I like to call linear regression the data scientist’s “workhorse.” It may not be sexy, but it’s a tried and proven technique that can be very useful. When the problem you’re trying to solve requires the prediction of a numeric response variable using multiple continuous (numeric) and/or categorical predictors,... Read more