Model evaluation is a critical component in supervised machine learning classification analyses. Traditional metrics do not currently incorporate case difficulty. This renders the classification results unbenchmarked for generalization. Item Response Theory (IRT) and Computer Adaptive Testing (CAT) with machine learning can benchmark datasets independent of the end-classification results. This provides high levels of case-level information regarding evaluation utility. To showcase, two datasets were used: 1) health-related and 2) physical science. For the health dataset a two-parameter IRT model, and for the physical science dataset a polytonomous IRT model, was used to analyze predictive features and place each case on a difficulty continuum. A CAT approach was used to ascertain the algorithms' performance and applicability to new data. This method provides an efficient way to benchmark data, using only a fraction of the dataset (less than 1%) and 22-60x more computationally efficient than traditional metrics. This novel metric, termed Machine Learning Capability (MLC) has additional benefits as it is unbiased to outcome classification and a standardized way to make model comparisons within and across datasets. MLC provides a metric on the limitation of supervised machine learning algorithms. In situations where the algorithm falls short, other input(s) are required for decision-making.

CALGARY, Alberta, July 06, 2021 (GLOBE NEWSWIRE) -- Quorum Information Technologies Inc. (TSX Venture: QIS) (Quorum) announced today that it is receiving advisory services and funding of up to $724,746 from the National Research Council of Canada Industrial Research Assistance Program (NRC IRAP) to support a research and development project to consolidate Quorum's dealership data and add machine learning capabilities to its Cloud-based applications. The NRC IRAP support is the next step in a process started in 2020 when Quorum launched QAnalytics – an enterprise reporting tool for the Quorum suite of products powered by Microsoft Power BI. QAnalytics is now utilized by 30% of Quorum's XSellerator Dealership Management System (DMS) customers. "QAnalytics has changed how we manage our 11 franchised dealerships in our auto group," stated Tim Davis, CEO of Davis Auto Group. "The real time metrics that QAnalytics provides for all aspects of our dealership's operations allow our management team to make confident, data-driven decisions." Quorum's next step is to strategically consolidate dealership data from its 1,025 customers on Microsoft Azure Synapse, enabling QAnalytics to deliver enhanced critical Business Intelligence insights into dealership operations and provide a consolidated dataset for Machine Learning projects.

… manufacturing (DFM) signoff with machine learning (ML) prediction capabilities. As part of the collaboration, the Cadence® Litho Physical Analyzer, …

The manufacturing industry is deeply impacted by the rise of machine learning projects. Originally coined by the German government in 2011, Industry 4.0 refers to the idea that the world has undergone the process of moving into a Fourth Industrial Revolution. Industry 4.0 is now widely accepted as the next paradigm for production, and if you're not already embracing the new revolution, then you are behind. Many outside of the tech industry are familiar with the First and Second from history textbooks but may not know where modern society now stands. The First revolution was a move from hand-made products to machine-made, the Second was marked by continuous processes and assembly lines, and the Third represented the widespread use of computers and robotics for industrial automation.

You can now ask questions in all supported language to generate results in real-time. You can use the property "Realtime" to get Analytics data from the last 30 minutes. For instance, you can monitor traffic to your website/app from a one-day promotion. You can also monitor the immediate effects of a campaign you are running or how recently made changes on your website or app is impacting them. You can ask for Analytics data in natural languages to get instant answers in Google Analytics 4.

Oracle is sprucing up its customer data platform, CX Unity, with a little machine learning. The platform will now support real-time behavioral data collection and personalization capabilities through Infinity, Oracle's digital streaming technology. Infinity captures web event, app and point-of-sale data to help brands build realistic representations of the customer journey. That previously required repeated and rigorous A/B testing, said Rob Tarkoff, EVP and general manager of Oracle Cloud CX and Oracle Data Cloud. "But by applying machine learning to that, you can come up with predictions and insights based on a holistic set of data, and it doesn't require you to do one-off activities," he said.

Machine Learning (ML) helps enterprises to predict user behavior, which helps acquire new customers, boost customer engagement and optimize value offerings. Machine Learning is defined as the application of Artificial Intelligence (AI) that makes systems capable of automatically learning and improving from experiences rather than explicit programming. This innovative technology focuses on developing software applications and models to access data generated from multiple sources and analyze it to learn new aspects for providing better solutions to real-world complex problems. In this blog, we have listed the top 10 Machine Learning companies that will meet your organizational needs and provide able assistance in your Digital Transformation journey. Also, look at what customers have to say about the quality of services delivered by such companies.

Thanx, a leading provider of digital guest engagement and retention tools for retailers and restaurants, today announced an enhanced offering for intelligently identifying and winning back valued guests with a high risk of churn. Thanx Personalized Winback uses an advanced ensemble-based Machine Learning algorithm to predict the churn likelihood of an individual guest based on nearly 40 data points, including spend and visit frequency compared to past behavior, recent and historic customer satisfaction, average check, LTV, likelihood of reacquisition and more. Once identifying the right at-risk guests, Thanx Personalized Winback automatically encourages those guests to return with personalized incentives. This press release features multimedia. Based on this cutting-edge Machine Learning capability, Thanx predicts an individual's likelihood of churn faster and with a higher degree of accuracy than traditional retention programs.

Editor's note: This is an excerpt from the full report. You can read the full survey report here. The survey was conducted on LinkedIn in April 2019 as a part of the University thesis prepared by A.Disbudak. The survey sought to evaluate the relevance of Machine Learning in operations today, assess the current state of Machine Learning adoption and to identify tools used for Machine Learning. The 140 qualified respondents represented a variety of company sizes from very small (one-person startups) to very large (multinationals with more than 10,000 employees).

Adoption of Machine Learning is growing significantly in business. More and more, the integration of Machine Learning is becoming an integral part of a digital transformation processes that businesses are looking to undergo. The advances in technology and the accessibility of Machine Learning capabilities, like for example TensorFlow or Cloud Services such as Google Cloud AI and operational tooling including Talend have helped combat the skills required to embrace Machine Learning concepts and accelerate delivery of solutions. Previously a discipline associated with scientists in white lab coats, Machine Learning is now becoming an increasingly mainstream activity. So instead of white lab coats you are more likely to find designer jeans and wearable devices associated with today's emerging army of machine learning developers.