Welcome!

Server Monitoring Authors: Yeshim Deniz, Liz McMillan, Pat Romanski, Carmen Gonzalez, Ken Schwaber

Blog Feed Post

A First Look at rxDForest()

by Joseph RIckert Last July, I blogged about rxDTree() the RevoScaleR function for building classification and regression trees on very large data sets. As I explaned then, this function is an implementation of the algorithm introduced by Ben-Haim and Yom-Tov in their 2010 paper that builds trees on histograms of data and not on the raw data itself. This algorithm is designed for parallel and distributed computing. Consequently, rxDTree() provides the best performance when it is running on a cluster: either an Microsoft HPC cluster or a Linux LSF cluster. rxDForest() (new with Revolution R Enterprise 7.0) uses rxDTree() to take the next logical step and implement a random forest type algorithm for building both classification and regression forests. Each tree of the ensemble constructed by rxDForest() is built with a bootstrap sample that uses about 2/3 of the original data. The data not used in builting a particular tree is used to make predictions with that tree. Each point of the original data set is fed through all of the trees that were built without it. The decision forest prediction for that data point is the statistical mode of the individual tree predictions. (For classification problems the prediction is a majority vote, for regression problems the prediction is the mean of the predictions.)  Only a couple of parameters need to be set to fit a decision forest. nTree specifies the number of trees to grow and  mTry spedifies the number of variables to sample as split candidates at each tree node. Of course, many more parameters can be set to control the algorithm, including the parameters that control the underlying rxDTree() algorithm. The following is a small example of the rxDForest() fucntion using the mortgage default dataset that can be downloaded from Revolution Analytic's website. Here are the first three lines of data.   creditScore houseAge yearsEmploy ccDebt year default1    615        10        5          2818 2000    02    780        34        5          3575 2000    0 3    735        12        1          3184 2000    0  The idea is to see if the variables creditScore, houseAge etc. are useful in predicting a default. The RevoScaleR R code in the file Download RxDForest  reads in the mortgage data, splits the data into a training file and a test file, uses rxDTree() to build a single tree (just to see what one looks like for this file) and plots the tree. Then rxDForest() is run against the training file to to build an ensemble model and this model run against the test file to make predictions. Finally, the code plots the ROC curve for the decision forest ensemble model. Here is what the first few nodes of the tree looks like. (The full tree is printed at the bottom of the code in the file above.) Call: rxDTree(formula = form1, data = "mdTrain", maxDepth = 5)File: C:\Users\Joe.Rickert\Documents\Revolution\RevoScaleR\mdTrain.xdf Number of valid observations: 8000290 Number of missing observations: 0 Tree representation: n= 8000290 node), split, n, deviance, yval * denotes terminal node 1) root 8000290 39472.30000 4.958445e-03 2) ccDebt< 9085.5 7840182 21402.25000 2.737309e-03 4) ccDebt< 7844 7384170 8809.46500 1.194447e-03  He is a plot of the right part of the tree drawn with RevoScaleR's creatTreeView() function that enables plot() to put the graph in your browser.     And, finally, here is the ROC curve for the decision Forest model. (The text output describing the model is also in the file containing the code.)   I plan to try rxDForest() out on a cluster with a bigger data set. When I do, I will let you know. 

Read the original blog entry...

More Stories By David Smith

David Smith is Vice President of Marketing and Community at Revolution Analytics. He has a long history with the R and statistics communities. After graduating with a degree in Statistics from the University of Adelaide, South Australia, he spent four years researching statistical methodology at Lancaster University in the United Kingdom, where he also developed a number of packages for the S-PLUS statistical modeling environment. He continued his association with S-PLUS at Insightful (now TIBCO Spotfire) overseeing the product management of S-PLUS and other statistical and data mining products.<

David smith is the co-author (with Bill Venables) of the popular tutorial manual, An Introduction to R, and one of the originating developers of the ESS: Emacs Speaks Statistics project. Today, he leads marketing for REvolution R, supports R communities worldwide, and is responsible for the Revolutions blog. Prior to joining Revolution Analytics, he served as vice president of product management at Zynchros, Inc. Follow him on twitter at @RevoDavid

IoT & Smart Cities Stories
Recently, REAN Cloud built a digital concierge for a North Carolina hospital that had observed that most patient call button questions were repetitive. In addition, the paper-based process used to measure patient health metrics was laborious, not in real-time and sometimes error-prone. In their session at 21st Cloud Expo, Sean Finnerty, Executive Director, Practice Lead, Health Care & Life Science at REAN Cloud, and Dr. S.P.T. Krishnan, Principal Architect at REAN Cloud, discussed how they built...
When talking IoT we often focus on the devices, the sensors, the hardware itself. The new smart appliances, the new smart or self-driving cars (which are amalgamations of many ‘things'). When we are looking at the world of IoT, we should take a step back, look at the big picture. What value are these devices providing. IoT is not about the devices, its about the data consumed and generated. The devices are tools, mechanisms, conduits. This paper discusses the considerations when dealing with the...
Bill Schmarzo, author of "Big Data: Understanding How Data Powers Big Business" and "Big Data MBA: Driving Business Strategies with Data Science," is responsible for setting the strategy and defining the Big Data service offerings and capabilities for EMC Global Services Big Data Practice. As the CTO for the Big Data Practice, he is responsible for working with organizations to help them identify where and how to start their big data journeys. He's written several white papers, is an avid blogge...
Business professionals no longer wonder if they'll migrate to the cloud; it's now a matter of when. The cloud environment has proved to be a major force in transitioning to an agile business model that enables quick decisions and fast implementation that solidify customer relationships. And when the cloud is combined with the power of cognitive computing, it drives innovation and transformation that achieves astounding competitive advantage.
Machine learning has taken residence at our cities' cores and now we can finally have "smart cities." Cities are a collection of buildings made to provide the structure and safety necessary for people to function, create and survive. Buildings are a pool of ever-changing performance data from large automated systems such as heating and cooling to the people that live and work within them. Through machine learning, buildings can optimize performance, reduce costs, and improve occupant comfort by ...
René Bostic is the Technical VP of the IBM Cloud Unit in North America. Enjoying her career with IBM during the modern millennial technological era, she is an expert in cloud computing, DevOps and emerging cloud technologies such as Blockchain. Her strengths and core competencies include a proven record of accomplishments in consensus building at all levels to assess, plan, and implement enterprise and cloud computing solutions. René is a member of the Society of Women Engineers (SWE) and a m...
JETRO showcased Japan Digital Transformation Pavilion at SYS-CON's 21st International Cloud Expo® at the Santa Clara Convention Center in Santa Clara, CA. The Japan External Trade Organization (JETRO) is a non-profit organization that provides business support services to companies expanding to Japan. With the support of JETRO's dedicated staff, clients can incorporate their business; receive visa, immigration, and HR support; find dedicated office space; identify local government subsidies; get...
With 10 simultaneous tracks, keynotes, general sessions and targeted breakout classes, @CloudEXPO and DXWorldEXPO are two of the most important technology events of the year. Since its launch over eight years ago, @CloudEXPO and DXWorldEXPO have presented a rock star faculty as well as showcased hundreds of sponsors and exhibitors! In this blog post, we provide 7 tips on how, as part of our world-class faculty, you can deliver one of the most popular sessions at our events. But before reading...
Digital Transformation: Preparing Cloud & IoT Security for the Age of Artificial Intelligence. As automation and artificial intelligence (AI) power solution development and delivery, many businesses need to build backend cloud capabilities. Well-poised organizations, marketing smart devices with AI and BlockChain capabilities prepare to refine compliance and regulatory capabilities in 2018. Volumes of health, financial, technical and privacy data, along with tightening compliance requirements by...
"Avere Systems deals with data performance optimization in the cloud or on-premise. Even to this day many organizations struggle with what we call the problem of data gravity - 'Where should I put the data?' - because the data dictates ultimately where the jobs are going to run," explained Scott Jeschonek, Director Cloud Solutions at Avere Systems, in this SYS-CON.tv interview at 21st Cloud Expo, held Oct 31 – Nov 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA.