ML Concepts – Evaluation Matrix and Equations

There are several classifiers can be used to identify the model’s effectiveness. You can assess using the evaluation metrics such as Accuracy, Recall, F1 Score, Precision, False Positive Rate (FPR), and True Positive Rate (TPR). Equations for these metrics are showing below. About The Author Dr. Pranay Jha Dr. Pranay Jha is a Cloud and…

There are several classifiers can be used to identify the model’s effectiveness. You can assess using the evaluation metrics such as Accuracy, Recall, F1 Score, Precision, False Positive Rate (FPR), and True Positive Rate (TPR).

  • Accuracy
  • Precision Score
  • F1 Score
  • Recall
  • Specificity     
  • ROC Curve

Equations for these metrics are showing below.

About The Author


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Architect’s Toolkit

About the Author

Dr. Pranay Jha is a Cloud and AI Consultant with 18+ years of experience in hybrid cloud, virtualization, and enterprise infrastructure transformation. He specializes in VMware technologies, multi-cloud strategy, and Generative AI solutions. He holds a PhD in Computer Applications with research focused on Cloud and AI, has published multiple research papers, and has been a VMware vExpert since 2016 and a VMUG Community Leader.

Discover more from Journal of Intelligent Infrastructure - By Dr Pranay Jha

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