APAC CIO Outlook
  • Home
  • CXO Insights
  • CIO Views
  • Vendors
  • News
  • Conferences
  • Whitepapers
  • Newsletter
  • Awards
Apac
  • Agile

    AI Healthcare

    Artificial Intelligence

    Aviation

    Bi and Analytics

    Big Data

    Cloud

    Cyber Security

    Digital Infrastructure

    Digital Marketing

    Digital Transformation

    Digital Twin

    Drone

    Internet of Things

    Low Code No Code

    Networking

    PropTech

    Remote Work

    Singapore Startups

    Smart City

    Startup

    Unified Communication

    Wireless

  • E-Commerce

    Education

    FinTech

    Healthcare

    Manufacturing

    Pharma and Life Science

    Retail

    Travel and Hospitality

  • Dell

    IBM

    Microsoft

    Salesforce

    SAP

  • Cognitive

    Compliance

    Contact Center

    Corporate Finance

    Data Center

    Data Integration

    Digital Asset Management

    Full Stack Development

    HR Technology

    IT Service Management

    Managed Services

    Procurement

    RegTech

    Travel Retail

Menu
    • SAP
    • Aviation
    • HR Technology
    • Manufacturing
    • Cloud
    • Data Center
    • Education
    • Salesforce
    • Digital Infrastructure
    • Bi and Analytics
    • Unified Communication
    • IBM
    • AI
    • MORE
    #

    Apac CIO Outlook Weekly Brief

    ×

    Be first to read the latest tech news, Industry Leader's Insights, and CIO interviews of medium and large enterprises exclusively from Apac CIO Outlook

    Subscribe

    loading

    THANK YOU FOR SUBSCRIBING

    • Home
    • News
    • Artificial Intelligence
    Editor's Pick (1 - 4 of 8)
    left
    The Right Technology And Reliable Partners; The Business Next Frontier

    Luke O'Brien, CIO, ISS Facility Services Australia & New Zealand

    Conquering Technological Transformation

    David Kennedy, Group CIO, Transaction Services Group

    How to Get to AI-first

    Ani Paul, CIO, ING Australia

    Legal Knowledge Management and the Rise of Artificial Intelligence

    Christopher Zegers, CIO, Lowenstein Sandler LLP

    Building an AI-Based Machine Learning for Global Economics

    Alexander Fleiss, CIO & CEO, Rebellion Research Partners LP

    Doing Analytics vs Scaling Analytics

    Ram Thilak, Global Head of Data Science and Analytics, Inchcape PLC

    The City of the Future Is Connected, Resilient, and Sustainable

    Chew Men Leong, President/Head, Urban Solutions, ST Engineering

    Changing the tester DNA -From tester to Software Development Engineer in Test (SDET)

    Bernd Bornhausen, Senior Manager QA, TD Insurance [NYSE: TD]

    right

    How To Know if Your Machine Learning Model Has Good Performance

    Apac CIO Outlook | Tuesday, May 21, 2019
    Tweet

    Machine learning is transforming the way businesses look at data and presenting new analytics opportunities for companies of all sizes. How firms leverage new technologies for machine learning in the industry will be a vital deciding factor in riding the waves of changes. Machine learning algorithms are deployed in various sectors, and tracking the development of it throughout its life cycle is crucial.

    The deployment of machine learning models involves a training phase, where a data scientist designs a model with excellent predictive capability based on historical information. This model is put into production and is expected to have a similar predictive performance during its deployment. There can be issues associated with the information deployed in the model such as incorrect models getting pushed, incoming data being corrupted, and incoming data no longer being resembled datasets used during training.

    At the heart of the ML model, is a Server and an Agent. The Server keeps a record of all the deployments across agents. Users can leverage built-in applications, health metrics, create new forms, or import their existing ML pipelines and computations using any of the popular programming languages.

    Machine learning model performance is, and ideas of what score an excellent model can achieve can be interpreted in the light of the skill scores of other models also trained on the same data. As a machine learning model performance is relative, it is crucial to create a robust baseline. A baseline is a simple procedure for making predictions on the predictive modeling problem.

    It is hard to find a model that can demonstrate works reliably well in making predictions on a firm’s specific dataset.

    See Also: Top Machine Learning Solution Companies

    tag

    Machine Learning

    Weekly Brief

    loading
    Top 10 Education Tech Solution Companies - 2021

    Featured Vendors

    Arlo

    John Mitchell, CEO

    Esri

    Jack Dangermond, President

    ON THE DECK

    Education 2021

    Top Vendors

    Education 2020

    Top Vendors

    Education 2019

    Top Vendors

    Education 2018

    Top Vendors

    Education 2017

    Top Vendors

    Education 2016

    Top Vendors

    Previous Next

    I agree We use cookies on this website to enhance your user experience. By clicking any link on this page you are giving your consent for us to set cookies. More info

    Read Also

    Benefits of Unified Communications

    Axie Infinity moves from Google reCAPTCHA to GeeTest

    Emerging Trends In Processing Big Data In The Future

    Benefits of IT Compliance

    Geetest, The Company Behind BINANCE CAPTCHA, Launched A New Product Adaptive CAPTCHA

    IBM ESS 3500 System to Tackle AI Adoption Challenges

    Loading...

    Copyright © 2022 APAC CIOoutlook. All rights reserved. Registration on or use of this site constitutes acceptance of our Terms of Use and Privacy and Anti Spam Policy 

    |  Sitemap |  Subscribe |   About us

    follow on linkedinfollow on twitter follow on rss
    This content is copyright protected

    However, if you would like to share the information in this article, you may use the link below:

    https://www.apacciooutlook.com/news/how-to-know-if-your-machine-learning-model-has-good-performance-nwid-6366.html