Machine Learning Fundamentals Classification Metrics

About

An introduction to Classification metrics and definition of the common ones.

Learning Objectives

You will be able to:

Compare regression and classification metrics.
Define common metrics and the confusion matrix.
Describe the ROC curve and the AUC score.

Content

Topic	Skills
Slides	- Introduction to Classification Metrics compared to regression metrics.
02 Classification Metrics	- Create a confusion matrix- - Calculate common classification metrics from the confusion matrix
03 ROC AUC	- Demonstrate the ROC curve and the AUC score

Prerequisites

Interpret and use Python dictionaries.
Build pandas DataFrames from dictionaries.
Perform simple data manipulation on pandas objects.
Build for and while loops in Python.
Use pip install for package management.
Create data visualizations in a noteboook using matplotlib and seaborn.
Build a linear regression model.
Understand bias / variance and implment train-test split.

References

📖 Reference Materials

Important note

Please ignore the _layouts folder and the config.yml file in the root of this repo. They are needed to make sure the repo renders well; please don’t change them or delete them and your repo will work fine :)