Confusion Matrices

Question 1

Accuracy

Answer 1

Fraction of data points correctly classified by a model;

Question 2

Confusion matrix

Answer 2

Visualization of classification model performance.

Question 3

Diagnostic odds ratio

Answer 3

Ratio of the odds that a data point in a certain category is correctly classified by a model, to the odds that a data point not in that category is incorrectly classified by the model; equal to

Question 4

Fall out

Answer 4

Fraction of data points not in a certain category that are incorrectly classified by a model Also called false positive rate

Question 5

False negative (FN)

Answer 5

Data point that a model incorrectly classifies as not being in a certain category. (“Negative” means the model classified it as not being in the category, and “False” means the model’s classification is incorrect.)
Sometimes abbreviated as “FN”.

Question 6

False negative rate

Answer 6

Fraction of data points in a certain category that are incorrectly classified by a model;. Also called miss rate.

Question 7

False positive (FP)

Answer 7

Data point that a model incorrectly classifies as being in a certain category. (“Positive” means the model classified it as being in the category, and “False” means the model’s classification is incorrect.) Sometimes abbreviated as “FP”.

Question 8

False positive rate

Answer 8

Fraction of data points not in a certain category that are incorrectly classified by a model; equal to 𝐹𝐹𝐹𝐹
𝑇𝑇𝑇𝑇+𝐹𝐹𝐹𝐹. Also called fall out.

Question 9

False omission rate

Answer 9

Fraction of data points the model classifies as not in a certain category, that are really in the category; equal to

Question 10

Hit rate

Answer 10

Fraction of data points in a certain category that are correctly classified by a model; equal to also called the true positive rate, sensitivity, and recall.

Question 11

Miss rate

Answer 11

Fraction of data points in a certain category that are incorrectly classified by a model; Also called false negative rate

Question 12

Negative likelihood ratio

Answer 12

Ratio of the fraction of data points in a certain category that are misclassified as not in the category, to the fraction of data points not in the category that are correctly classified as not being in the category;
equal to (1-sensitivity)/specificity

Question 13

Negative predictive value

Answer 13

Fraction of data points classified as not in a certain category that are really not in that category; equal to

Question 14

Positive likelihood ratio

Answer 14

Ratio of the fraction of data points in a certain category that are correctly classified as being in that category, to the fraction of data points not in the category that are incorrectly classified as being in the category; equal to sensitivity/(1-specificity)

Question 15

Positive predictive value

Answer 15

Fraction of data points classified as being in a certain category that are really in that category; . Also called precision.

Question 16

Precision

Answer 16

In analytics, the fraction of data points classified as being in a certain category that are really in that category. Also called positive predictive value.

Question 17

Sensitivity

Answer 17

Fraction of data points in a certain category that are correctly classified by a model also called the true positive rate, hit rate,and recall.

Question 18

Specificity

Answer 18

Fraction of data points not in a certain category that are correctly classified by a model also called the true negative rate.

Question 19

True negative (TN)

Answer 19

Data point that a model correctly classifies as not being in a certain category. (“Negative” means the model classified it as not being in the category, and “True” means the model’s classification is correct.) Sometimes abbreviated as “TN”.

Question 20

True negative rate

Answer 20

Fraction of data points not in a certain category that are correctly classified by a model; also called specificity.

Question 21

True positive (TP)

Answer 21

Data point that a model correctly classifies as being in a certain category. (“Positive” means the model classified it as being in the category, and “True” means the model’s classification is correct.) Sometimes abbreviated as “TP”.

Question 22

True positive rate

Answer 22

Fraction of data points in a certain category that are correctly classified by a model also called sensitivity, hit rate, and recall.