This tag relates to the discussions on the development of analytics and business intelligence solutions, visualization, KPI and other business metrics management.
When we have to predict the value of a categorical (or discrete) outcome we use logistic regression. I believe we use linear regression to also predict the value of an outcome given the input values.
Then, what is the difference between the two methodologies?
A simple data analysis example created in IntegratedML and Dashboard
Based on InterSystems' Integrated ML technology and Dashboard, automatically generate relevant predictions and BI pages based on uploaded CSV files. The front and back ends are completed in Vue and Iris, allowing users to generate their desired data prediction and analysis pages with simple operations and make decisions based on them.
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A full cycle project, from initial data initialization, daily update products from the official website, a service for sending a course on request and receiving a schedule for any period.
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I have recently studied deepsee and developed few dashboards needed for our web app users. I am trying to embed them in our existing web app which uses angular with delegated user access. I need to embed the native IRIS dashboard into it. ( I can't use Highcharts or any other js tools).
How do I setup the dashboards to work with delegated authentication (Without providing access to management portal or other parts) ? Also should I use the default csp/{Namespace}/_DeepSee.UserPortal.DashboardViewer.zen? or any other web application URL ?
A few months ago, I faced a significant challenge: streamlining the handling of business logic in our application. My goal was to extract the business logic from the code and hand it over to analysts. Dealing with a multitude of rules could easily result in a code littered with countless "if" statements, especially if the coder lacked an understanding of cyclomatic complexity. Such code becomes a source of pain for those working with it—difficult to write, test, and develop.
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Watch this video to learn about some recent machine learning engagements InterSystems is driving, including TrakCare predictive analytics and examples from our Developer Community and partners:
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Episode 17 of Data Points features a roundtable conversation with Carmen Logue, Benjamin De Boe, and Thomas Dyar about the Analytics & AI area of the InterSystems technology stack. Learn from these product experts about the various technologies and partnerships that exist within the Analytics & AI space at InterSystems, how some customers use these tools, and what might be coming in the future.
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Sometimes you when create a pivot in Analyzer and see the result that shouldn't exist - don't run to create a WRC ticket, try to call the IRIS BI reset function first:
As said in the previous article about the iris-fhir-generative-ai experiment, the project logs all events for analysis. Here we are going to discuss two types of analysis covered by analytics embedded in the project:
When creating pivots in DeepSee, there is the ability to group date dimensions by Day, Week, Month, Quarter, Year. For some of our reporting the Week option is desired. However the display looks like the image shown below. Notice the "YYYYW##" formatting.
Is there any ability within DeepSee to modify the display of grouped week dates? E.g. instead of 2020W14 it could be 03/30/2020-04/05-2020, or even just the start or end date, etc.
In our latest episode of Data Points, @Brenna Quirk and I had a conversation with @Benjamin De Boe about the all-new columnar storage feature in InterSystems IRIS. Benjamin tells us a bit about what columnar storage is, why it's important for InterSystems IRIS users running analytical queries, and how you can learn more. Take a listen!
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With the improvement of living standards, people pay more and more attention to physical health. And the healthy development of children has become more and more a topic of concern for parents. The child's physical development can be reflected from the child's height and weight. Therefore, it is of great significance to predict the height and weight in a timely manner.
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