Machine learningLesson 1 of 1 in the course Model evaluation
Confusion Matrices: Binary Outcomes, Imbalance, and Extensions
5 flashcards · 5 quiz questions · 5 match pairs · 5 fill-in sentences · English
Lesson progress
0 / 5
Your progress is kept in this browser only.Sign up to save it
Summary
A confusion matrix summarizes how a classifier's predictions compare with the actual classes. It helps show not only how many predictions are correct, but also which kinds of mistakes the classifier makes.
A confusion matrix, also called an error matrix, is a table that compares actual classifications with predicted classifications. In a common layout, rows represent actual classes and columns represent predicted classes. Some sources reverse this layout, so always check the labels.
All flashcards
Quiz questions
Based on the Wikipedia article Confusion matrix, licensed CC BY-SA 4.0. Adapted into a summary, flashcards and quizzes.
More in Machine learning
All Machine learning lessonsMachine learningAutoencoder Architectures, Training, Regularization, and Applications5 flashcards · 5 quiz questions · 4 gamesPlay free →Machine learningSupport Vector Machines: Margins, Kernels, Optimization, and Extensions5 flashcards · 5 quiz questions · 4 gamesPlay free →Machine learningNeural-Network Activation Functions: Properties and Families5 flashcards · 5 quiz questions · 4 gamesPlay free →Machine learningHierarchical Clustering: Linkage, Dendrograms, and Scalable Methods5 flashcards · 5 quiz questions · 4 gamesPlay free →Machine learningNeural Networks: Architectures, Training, History, and Applications5 flashcards · 5 quiz questions · 4 gamesPlay free →