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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
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Hands-On
Python for Machine Learning
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GitBook
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.
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Accuracy = \frac{number \space correct \space predictions}{number \space of \space samples}
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Sensitivity
: also known as
recall
, is computed as the fraction of true positives that are correctly identified.
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Sensitivity = \frac{number \space of \space true \space positives}{number \space of \space true \space positives + number \space of \space false \space negatives}
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Precision
: computed as the fraction of retrieved instances that are relevant.
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Precision = \frac{number \space of \space true \space positives}{number \space of \space true \space positives + number \space of \space false \space positives}
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Specificity
: computed as the fraction of true negatives that are correctly identified.
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Specificity = \frac{number \space of \space true \space negatives}{number \space of \space true \space negatives + number \space of \space false \space positives}
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🚧
This section is still under construction!
Train and Evaluate - Previous
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ML Life Cycle
Last modified
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