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How to Measure Quality While Training the Machine Learning Models

To know how to measure quality while training the machine learning models check this presentation that well-defines about the various parameters to check the quality of training data sets used in machine learning process to develop AI-based various models and business applications. The PPT by Cogito is showing how it is also beneficial to measure the quality of such data sets in machine learning. <br><br>Visit: http://bit.ly/2TLE6kH<br> <br>

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How to Measure Quality While Training the Machine Learning Models

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  1. How to Measure Quality While Training the Machine Learning Models

  2. Training Machine Learning Models The quality of data in training the machine learning models is one the most important factor while developing such models.

  3. Consistency or Accuracy, which one is important Quality here is directly related to consistency and accuracy, this is not just how correct a data or label is but also how frequently it is correct.

  4. About Us Here in this article you will learn about the definitions of quality, consistency and accuracy and why quality matters in training the machine learning models. Benefits of Quality Check in Training the Machine Learning 1. Monitor the consistency and accuracy of training data. 2. Quickly troubleshoot quality related errors. 3. Improve labeler instructions, on-boarding, and training. 4. Better understanding of their project on what and how to label.

  5. Our Review Review is another method to check the accuracy and this done by trusted experts after completion of labeling. Quality Workflows Idyllically, quality assurance is an automated process, that works continuously, during your training data development and improvement processes. Such data labeling consensus and benchmark features, consistency and accuracy tests with amazing results. As this test allows you to customize the section of your data to test and the number of labelers that will annotate the test data with right process. you can automate

  6. Benchmarks This process of data quality and to follow this process of testing the quality you have to create a new Benchmark by starring an existing label. Consensus Consensus actually measures the rate of agreement between multiple annotators that is usually humans or machines. To calculate the consensus score divide the sum of agreeing labels by the total number of labels per asset and get the accurate score results.

  7. Summing-up While applications, creating a training data is one the most costly consistently monitoring training data quality improves the chance of having a well- performing model even develop for the first time. While getting labels right the first time, it is cheaper than the cost of identifying and redoing work to solve such problems. building the machine learning components. And

  8. Cogito is the most suitable company providing the high-quality training data for machine learning.

  9. Thank You Cogito +1 516 342 5749 info@cogitotech.com www.cogitotech.com

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