Tag: regression

Feed: Puppet.com Blog RSS Feed. Author: ; Our mission at Puppet is to empower people through software, and by “people” we mean “IT professionals whose job is to deliver applications and services to internal and external end-users.” The users of our software work at over 37,000 organizations worldwide, and have traditionally had titles like sysadmin or operations engineer. But this has been changing: Over the last few years, we’ve seen a transition in how organizations deliver applications and services, moving from functional teams (Development, QA, and Operations) with a linear software delivery lifecycle, into cross-functional teams that rapidly iterate to ... Read More

Feed: R-bloggers. Author: Business Science. [This article was first published on business-science.io, 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. Product price estimation and prediction is one of the skills I teach frequently – It’s a great way to analyze competitor product information, your own company’s product data, and develop key insights into which product features influence product prices. Learn how to model product car prices and calculate depreciation curves using the brand ... Read More

Feed: R-bloggers. Author: Keith Goldfeld. [This article was first published on ouR data generation, 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 ROC (receiver operating characteristic) curve visually depicts the ability of a measure or classification model to distinguish two groups. The area under the ROC (AUC), quantifies the extent of that ability. My goal here is to describe as simply as possible a process that serves as a foundation for the ... Read More

Feed: R-bloggers. Author: Learning Machines. We already covered the so-called Accuracy-Interpretability Trade-Off which states that oftentimes the more accurate the results of an AI are the harder it is to interpret how it arrived at its conclusions (see also: Learning Data Science: Predicting Income Brackets). This is especially true for Neural Networks: while often delivering outstanding results, they are basically black boxes and notoriously hard to interpret (see also: Understanding the Magic of Neural Networks). There is a new hot area of research to make black-box models interpretable, called Explainable Artificial Intelligence (XAI), if you want to gain some intuition ... Read More

Feed: R-bloggers. Author: R Views. [This article was first published on R Views, 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. One hundred fifty-two packages made it to CRAN in December. Here are my “Top 40” picks in ten categories: Data, Genomics, Machine Learning, Mathematics, Medicine, Science, Statistics, Time Series, Utilities, and Visualization. Data climate v0.3.0: Provides access to meteorological and hydrological data from OGIMET, University of Wyoming – atmospheric vertical profiling data, ... Read More

Feed: R-bloggers. Author: John. Statisticians often come across outliers when working with datasets and it is important to deal with them because of how significantly they can distort a statistical model. Your dataset may havevalues that are distinguishably different from most other values, these arereferred to as outliers. Usually, an outlier is an anomaly that occurs due tomeasurement errors but in other cases, it can occur because the experimentbeing observed experiences momentary but drastic turbulence. In either case, itis important to deal with outliers because they can adversely impact theaccuracy of your results, especially in regression models. In this tutorial, ... Read More

Feed: R-bloggers. Author: R on Stats and R. [This article was first published on R on Stats and R, 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. Simple linear regression is a statistical method to summarize and study relationships between two variables. When more than two variables are of interest, it is referred as multiple linear regression. In this article, we focus only on a Shiny app which allows to perform simple linear ... Read More

Feed: Planet MySQL; Author: Valeriy Kravchuk; I'd like to continue reviewing MySQL bug reports from Community users that I considered interesting and subscribed to. Unlike in the previous post in this series, I am not going to check test cases on any competitor product, but will use only recently released MySQL 5.7.29 and 8.0.19 for checks, if any. This time I'll concentrate on bugs reported in November 2019.As usual, I mostly care about optimizer, InnoDB and replication related bugs. Here is the list:Bug #97476 - "Range optimizer skips rows". This bug reported by Ilya Raudsepp looks like a clear regression in ... Read More

Feed: SAS Blogs. Author: Christa Cody. This blog is part of a series on SAS Visual Data Mining and Machine Learning (VDMML). If you're new to SAS VDMML and you want a brief overview of the features available, check out my last blog post! This blog will discuss types of missing data and how to use imputation in SAS VDMML to improve your predictions.  Imputation is an important aspect of data preprocessing that has the potential to make (or break) your model. The goal of imputation is to replace missing values with values that are close to what the missing value ... Read More

Feed: Microsoft Azure Blog. Author: James Ding. Anomaly detection plays a vital role in many industries across the globe, such as fraud detection for the financial industry, health monitoring in hospitals, fault detection and operating environment monitoring in the manufacturing, oil and gas, utility, transportation, aviation, and automotive industries. Anomaly detection is about finding patterns in data that do not conform to expected behavior. It is important for decision-makers to be able to detect them and take proactive actions if needed. Using the oil and gas industry as one example, deep-water rigs with various equipment are intensively monitored by hundreds ... Read More