AI

Question 1

binary classification

Answer 1

A type of classification task that outputs one of two mutually exclusive classes. For example, a machine learning model that evaluates email messages and outputs either “spam” or “not spam” is a binary classifier.

Question 2

classification model

Answer 2

A type of machine learning model for distinguishing among two or more discrete classes. For example, a natural language processing classification model could determine whether an input sentence was in French, Spanish, or Italian. Compare with regression model.

Question 3

regression model

Answer 3

A type of model that outputs continuous (typically, floating-point) values. Compare with classification models, which output discrete values, such as “day lily” or “tiger lily.”

Question 4

TPR

Answer 4

True positive rate. (synonym for recall)

TPR = TP / (TP + FN)

Of all the positive label, what the percentage is to be right true positive.

Question 5

FPR

Answer 5

False positive rate

FPR = FP / (FP + TN)

Of all the negative label, what the percentage is to be false positive.

Question 6

AUC

Answer 6

AUC (Area under the ROC Curve)

An evaluation metric that considers all possible classification thresholds.

The Area Under the ROC curve is the probability that a classifier will be more confident that a randomly chosen positive example is actually positive than that a randomly chosen negative example is positive.

Question 7

ROC

Answer 7

ROC (receiver operating characteristic) Curve

A curve of true positive rate vs. false positive rate at different classification thresholds. See also AUC.