Tag: Classification

Feed: R-bloggers. Author: Bob Muenchen. [This article was first published on R – r4stats.com, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.The BlueSky Statistics graphical user interface (GUI) for the R language has added quite a few new features (described below). I’m also working on a BlueSky User’s Guide, a draft of which you can read about and download here. Although I’m spending a lot of time on BlueSky, I still plan to be ... Read More

Feed: The Internet of Things on AWS – Official Blog. Author: Amanda Lowe. There are billions of devices in homes, factories, oil wells, hospitals, cars, and thousands of other places. Companies are discovering that these “things” not previously connected to the Internet can provide intelligence into new areas of their business. The result? All aspects of an enterprise can be data-driven, which leads to insights that they can use to improve the customer experience, increase operational efficiency, and fuel innovation. Companies can understand their business operations better and make accurate decisions about their products, processes, and customer satisfaction faster than ... Read More

Feed: R-bloggers. Author: Rstats on Jakub Nowosad's website. The raceland package implements a computational framework for a pattern-based, zoneless analysis and visualization of (ethno)racial topography.The main concept in this package is a racial landscape (RL). It consists of many large and small patches (racial enclaves) formed by adjacent raster grid cells having the same race categories. The distribution of racial enclaves creates a specific spatial pattern, which can be quantified by two metrics (entropy and mutual information) derived from the Information Theory concept (IT). Entropy is the measure of racial diversity and mutual information measures racial segregation.Methods in the raceland ... Read More

Feed: R-bloggers. Author: free range statistics - R. Visualising growth in occupations in one industryA chart is doing the rounds purporting to show the number of administrators working in health care in the USA has grown much faster than the number of physicians – more than 3,000% growth from 1970 to 2009 for administrators (allegedly) compared to about 90% or so for physicians. I don’t much like the original chart so I’ve relegated it to the bottom of this post. It presumably dates from the time of the debates about the introduction of the Affordable Care Act (aka ‘Obamacare’). I ... Read More

Feed: R-bloggers. Author: Przemyslaw Biecek. fairmodels: let’s fight with biased Machine Learning models (part 1 — detection)Author: Jakub WiśniewskiTL;DRThe fairmodels R Package facilitates bias detection through model visualizations. It implements few mitigation strategies that could reduce the bias. It enables easy to use checks for fairness metrics and comparison between different Machine Learning (ML) models.Longer versionFairness in ML is a quickly emerging field. Big companies like IBM or Google developed some tools already (see AIF360) with growing community of users. Unfortunately, there aren’t many tools enabling to discover bias and discrimination in machine learning models created in R. Therefore, checking the fairness of ... Read More

Feed: Teradata Blog. AWS Glue now supports streaming ETL. This feature makes it easy to set up continuous ingestion pipelines that prepare streaming data on the fly and make it available for analysis in seconds. Streaming ETL jobs in AWS Glue can consume data from streaming sources likes Amazon Kinesis and Apache Kafka, clean and transform those data streams in-flight, and continuously load the results into Amazon S3 data lakes, data warehouses, or other data stores. Customers can use this feature to process event data like IoT event streams, clickstreams, and network logs. Customers who want to use Teradata Vantage ... Read More

Feed: What's New. Author: Daniel Grady, Sasha Singh. Bring in the right data The more data you bring in to Prep Builder while you’re in interactive mode, the more computationally “expensive” your flow will be. While there’s no row limit for working with data sets in Prep Builder, there are considerations to optimize performance—great power comes great responsibility after all. A simple yet powerful way to ensure high performance is working with only the data you need. When you run your flow, whether in Prep Builder or via Prep Conductor, changes are always applied to the entire data set and ... Read More

Feed: SAS Blogs. Author: Rick Wicklin. When you write a program that simulates data from a statistical model, you should always check that the simulation code is correct. One way to do this is to generate a large simulated sample, estimate the parameters in the simulated data, and make sure that the estimates are close to the parameters in the simulation. For regression models that include CLASS variables, this task requires a bit of thought. The problem is that categorical variables can have different parameterizations, and the parameterization determines the parameter estimates. Recently I read ... Read More

Feed: IBM Big Data & Analytics Hub - All Content; Author: dan-cheuvront; Follow @IBMDataThis story is part of Analytics Heroes, a series of profiles on leaders transforming the future of business analytics. Stephanie Wagenaar is a senior manager at BOLD, based in Baarn, the Netherlands. BOLD is a consultancy firm that supports businesses by combining strategies with their hands-on “analyze, advise, execute” approach, and offering “turnarounds, transformations, and transactions.” Stephanie focuses on the transformation aspect of this equation in their newest offering launching in autumn 2020: BOLD.digital. With this new venture, Stephanie continues to help businesses develop and implement more ... Read More

Feed: AWS Architecture Blog. IntroductionIf you’re an enterprise organization, especially in a highly regulated sector, you understand the struggle to innovate and drive change while maintaining your security and compliance posture. In particular, your banking customers’ expectations and needs are changing, and there is a broad move away from traditional branch and ATM-based services towards digital engagement. With this shift, customers now expect personalized product offerings and services tailored to their needs. To achieve this, a broad spectrum of analytics and machine learning (ML) capabilities are required. With security and compliance at the top of financial service customers’ agendas, being ... Read More