Tag: predicting

Feed: CDH – Cloudera Engineering Blog. Author: Cy Jervis. Modeling EHR Data in Healthcare In this case study, we take a look at modeling electronic health record (EHR) data with deep learning and Deeplearning4j (DL4J). We draw inspiration from recent research showing that carefully designed neural network architectures can learn effectively from the complex, messy data collected in EHRs. Specifically, we describe how to train an  long short-term memory recurrent neural network (LSTM RNN) to predict in-hospital mortality among patients hospitalized in the intensive care unit (ICU). Of particular note, we show that even for a dataset orders of magnitude ... Read More

Feed: R-bloggers. Author: business-science.io - Articles. Employee turnvover (attrition) is a major cost to an organization, and predicting turnover is at the forefront of needs of Human Resources (HR) in many organizations. Until now the mainstream approach has been to use logistic regression or survival curves to model employee attrition. However, with advancements in machine learning (ML), we can now get both better predictive performance and better explanations of what critical features are linked to employee attrition. In this post, we’ll use two cutting edge techniques. First, we’ll use the h2o package’s new FREE automatic machine learning algorithm, h2o.automl(), to ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Vincent Granville. This is our third post of a new series featuring articles published long ago. We manually selected articles that were most popular or overlooked, time-insensitive (for instance we eliminated articles about data science products because software packages and platforms have evolved so much over the last few years) and we only kept articles that still make sense and are useful today. Our previous edition can be found here.  25 Timeless Data Science Articles Machine Learning in Parallel: Three Algorithms + Marrying computer science, statistics and domain expertize  ETL, ELT and Data ... Read More

Feed: Planet big data. Author: JeanFrancoisPuget. Machine Learning and Deep Learning are very promising technologies.  Every week comes with its new hyped successes.  Yet, when it comes to applying machine learning and deep learning many people keep making the same mistakes.  Here is one that is particularly troublesome: people often miss that you need to provide examples to learn from.  They expect systems to learn from raw data without any supervision or feedback.  I can't blame them, as many proponents of machine learning, deep learning, or artificial intelligence just speak about how systems learned from data, without entering the gory ... Read More

Feed: R-bloggers. Author: Keith Goldfeld. Inspired by a free online course titled Complier Average Causal Effects (CACE) Analysis and taught by Booil Jo and Elizabeth Stuart (through Johns Hopkins University), I’ve decided to explore the topic a little bit. My goal here isn’t to explain CACE analysis in extensive detail (you should definitely go take the course for that), but to describe the problem generally and then (of course) simulate some data. A plot of the simulated data gives a sense of what we are estimating and assuming. And I end by describing two simple methods to estimate the CACE, ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Vincent Granville. After Equifax's massive data breach - social security number, date of birth, and address from 143 million Americans stolen by cyber criminals - the question is: Can a financial institution be liable for using wrong Equifax data? One would expect that this Equifax event will result in an increase in ID theft. Banks trust credit scores provided by Equifax and other agencies. Indeed, they rely mostly on these numbers to offer or decline a loan. What if they offer a loan to a (fake) army colonel that is not even ... Read More

Feed: All - O'Reilly Media. Author: Tobi Bosede. Wrong Way Go Back (source: Johnny Jet on Flickr) Check out Tobi Bosede’s session “Big data analysis of futures trades” at the Strata Data Conference in New York City, September 25-28, 2017, for more tips on how to work efficiently in Spark, and how it can be used in predicting trade volume. Registration is now open. My upcoming Strata Data NYC 2017 talk about big data analysis of futures trades is based on research done under the limited funding conditions of academia. This meant that I did not have an infrastructure team, ... Read More

Feed: Latest imported feed items on Analytics Matters. In the last instalment of this blog series, we discussed objectives and accuracy in machine learning. And we described two crucial tests for the utility of a machine learning model: The model must be sufficiently accurate and we must be able to deploy the model so that it can produce actionable outputs from the available data. We then introduced a real-world scenario — predicting train failures up to 36 hours in advance of their occurrence using sensor data — to illustrate the application of those tests. But how did we decide which ... Read More

Feed: Latest imported feed items on Analytics Matters. We get to go to a lot of conferences. And we’re always amazed at how many vendors and commentators stand up at events and trade shows and say things like, “The objective of analytics is to discover new insight about the business”. Let us be very clear. If the only thing that your analytic project delivers is insight, it has almost certainly failed. Your objective must not be merely to discover something that you didn’t know, or to quantify something that you thought you did — rather it must be to use ... Read More

Feed: All - O'Reilly Media. Author: Josh Patterson, Kirit Basu. Building out a production-grade real-time image classification system (source: Josh Patterson and Kirit Basu, used with permission) To learn more about creating real-time streaming image classification systems, check out Josh Patterson’s and Kirit Basu’s session “Real-time image classification: Using convolutional neural networks on real-time streaming data” at the Strata Data Conference in New York City, Sept. 25-28, 2017. Registration is now open. Integrating neural networks and convolutional neural networks into a production-ready enterprise application can be a challenge in itself, separate from the modelling task. In addition to the infrastructure ... Read More