Introduction to the Confusion Matrix

A confusion matrix, also known as an error matrix, uses a special table to help visualize the performance of your classification model. That way, you can easily see how successful your model was when predicting the class. In this introduction, we give you a brief overview of what a confusion matrix is, how to create your matrix, and why you should use it.

Topics include: true positives and negatives, target classes, and predictive models.

Learn more about Classification Models:
Introduction to Classification Models
Precision, Recall and F1 in Classification
One Versus One vs. One Versus All in Classification

Complete Series:
Data Science in Minutes

More Data Science Material:
[Video] Introduction to Big Data, Data Science and Predictive Analytics
[Video] Predictive Modeling with R and Azure ML
[Blog] Azure ML Tutorial – Build a Predictive Model


Rebecca Merrett
About The Author
- Rebecca holds a bachelor’s degree of information and media from the University of Technology Sydney and a post graduate diploma in mathematics and statistics from the University of Southern Queensland. She has a background in technical writing for games dev and has written for tech publications.


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