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When the data has a lot of features that interact in complicated non-linear ways, it is hard to find a global regression model i.e. a single predictive formula that holds over the entire dataset. An alternative approach is to partition the space into smaller regions, then into sub-partitions (recursive partitioning) until each chunk can be explained with a simple model. There are two main types o
Product(s): Tableau Desktop, Tableau Public, Tableau Public Premium Version(s): All Last Modified Date: 19 Jun 2014 Article Note: This article is no longer actively maintained by Tableau. We continue to make it available because the information is still valuable, but some steps may vary due to product changes. You can use market basket analysis to discover and understand customer purchasing beha
In my post on Tableau website earlier this year, I included an example of multiple linear regression analysis to demonstrate taking advantage of parameters to create a dashboard that can be used for What-If analysis. In that example, the model fitting was done inside the same calculation as the predictions, in order to provide a self-contained example that just works. However in real-life, once cr
Elon Reeve Musk (/ËiËlÉn/; born June 28, 1971) is a businessman known for his key roles in the space company SpaceX and the automotive company Tesla, Inc. Other involvements include ownership of X Corp., the company that operates the social media platform X (formerly Twitter), and his role in the founding of the Boring Company, xAI, Neuralink, and OpenAI. In November 2024, President-elect Donald T
This article includes a list of general references, but it lacks sufficient corresponding inline citations. Please help to improve this article by introducing more precise citations. (April 2009) (Learn how and when to remove this message) In medical research, epidemiology, social science, and biology, a cross-sectional study (also known as a cross-sectional analysis, transverse study, prevalence
AbstractAs one of the most successful approaches to building recommender systems, collaborative filtering (CF) uses the known preferences of a group of users to make recommendations or predictions of the unknown preferences for other users. In this paper, we first introduce CF tasks and their main challenges, such as data sparsity, scalability, synonymy, gray sheep, shilling attacks, privacy prote
Marketing is at an inflection point. Hereâs why thatâs an opportunity
Note: This is a pre-publication version of my article published as part of my bi-monthly column in the ACM CHI magazine, Interactions. The final, published version is available on the Interactions website: interactions.acm.org, Interactions, volume 17, issue 2. I urge you to read the entire magazine -- subscribe. It's a very important source of design information. See their website at interactions
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