ProductizeML
  • ProductizeML
  • Introduction
    • Objectives
    • About the Course
    • Guidelines
    • Syllabus
    • After Completion
  • Machine Learning
    • Why ML, and why now
    • Supervised Learning
    • Unsupervised Learning
    • Deep Learning
    • ML Terminology
  • Data Management
    • Data Access
    • Data Collection
    • Data Curation
  • Train and Evaluate
    • Framework and Hardware
    • Training Neural Networks
    • Model Evaluation
  • Productize It
    • ML Life Cycle
    • Business Objectives
    • Data Preparation
    • Model Development
    • Train, Evaluate, and Deploy
    • A/B Testing
    • KPI Evaluation
    • PM Terminology
  • Resources
    • Readings
    • Courses
    • Videos
  • Hands-On
    • Python for Machine Learning
      • Python Installation
        • MacOS
        • Linux
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  1. Train and Evaluate

Model Evaluation

You will learn: how to assess to evaluation of ML models.

Classification metrics

  • Accuracy: computes the number of correct predictions divided by the total number of samples.

    Accuracy=number correct predictionsnumber of samplesAccuracy = \frac{number \space correct \space predictions}{number \space of \space samples}Accuracy=number of samplesnumber correct predictions​

  • Sensitivity: also known as recall, is computed as the fraction of true positives that are correctly identified.

    Sensitivity=number of true positivesnumber of true positives+number of false negativesSensitivity = \frac{number \space of \space true \space positives}{number \space of \space true \space positives + number \space of \space false \space negatives}Sensitivity=number of true positives+number of false negativesnumber of true positives​

  • Precision: computed as the fraction of retrieved instances that are relevant.

    Precision=number of true positivesnumber of true positives+number of false positivesPrecision = \frac{number \space of \space true \space positives}{number \space of \space true \space positives + number \space of \space false \space positives}Precision=number of true positives+number of false positivesnumber of true positives​

  • Specificity: computed as the fraction of true negatives that are correctly identified.

    Specificity=number of true negativesnumber of true negatives+number of false positivesSpecificity = \frac{number \space of \space true \space negatives}{number \space of \space true \space negatives + number \space of \space false \space positives}Specificity=number of true negatives+number of false positivesnumber of true negatives​

🚧 This section is still under construction!

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Last updated 2 years ago

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