IYSE 6501 Glossary

Question 1

Algorithm

Answer 1

Step-by-step procedure designed to carry out a task.

Question 2

Change detection

Answer 2

Identifying when a significant change has taken place in a process.

Question 3

Classification

Answer 3

The separation of data into two or more categories, or (a point’s
classification) the category a data point is put into.

Question 4

Classifier

Answer 4

A boundary that separates the data into two or more categories. Also
(more generally) an algorithm that performs classification.

Question 5

Cluster

Answer 5

A group of points identified as near/similar to each other.

Question 6

Cluster center

Answer 6

In some clustering algorithms (like 𝑘𝑘-means clustering), the central
point (often the centroid) of a cluster of data points.

Question 7

Clustering

Answer 7

Separation of data points into groups (“clusters”) based on
nearness/similarity to each other. A common form of unsupervised
learning.

Question 8

CUSUM

Answer 8

Change detection method that compares observed distribution mean
with a threshold level of change. Short for “cumulative sum”

Question 9

Deep learning

Answer 9

Neural network-type model with many hidden layers

Question 10

Dimension

Answer 10

A feature of the data points (for example, height or credit score). (Note
that there is also a mathematical definition for this word.

Question 11

EM algorithm

Answer 11

Expectation-maximization algorithm.

Question 12

Expectation-maximization
algorithm (EM algorithm)

Answer 12

General description of an algorithm with two steps (often iterated),
one that finds the function for the expected likelihood of getting the
response given current parameters, and one that finds new parameter
values to maximize that probability.

Question 13

Heuristic Algorithm

Answer 13

Algorithm that is not guaranteed to find the absolute best (optimal)
solution

Question 14

𝑘-means algorithm

Answer 14

Clustering algorithm that defines 𝑘𝑘 clusters of data points, each
corresponding to one of 𝑘𝑘 cluster centers selected by the algorithm.

Question 15

k𝑘-Nearest-Neighbor (KNN)

Answer 15

Classification algorithm that defines a data point’s category as a
function of the nearest 𝑘𝑘 data points to it.

Question 16

Kernel

Answer 16

a type of function that computes the similarity between two inputs;
thanks to what’s (really!) sometimes known as the “kernel trick”,
nonlinear classifiers can be found almost as easily as linear ones.

Question 17

Learning

Answer 17

Finding/discovering patterns (or rules) in data, often that can be
applied to new data.

Question 18

Machine

Answer 18

Apparatus that can do something; in “machine learning”, it often refers to both an algorithm and the computer it’s run on. (Fun fact: before
computers were developed, the term “computers” referred to people
who did calculations quickly in their heads or on paper!)

Question 19

Margin

Answer 19

For a single point, the distance between the point and the classification
boundary; for a set of points, the minimum distance between a point
in the set and the classification boundary. Also called the separation

Question 20

Machine learning

Answer 20

Use of computer algorithms to learn and discover patterns or structure
in data, without being programmed specifically for them

Question 21

Misclassified

Answer 21

Put into the wrong category by a classifier

Question 22

Neural network

Answer 22

A machine learning model that itself is modeled after the workings of
neurons in the brain.

Question 23

Supervised learning

Answer 23

Machine learning where the “correct” answer is known for each data
point in the training set.

Question 24

Support vector

Answer 24

In SVM models, the closest point to the classifier, among those in a
category. (Note that there is a more-technical mathematical definition
too.)

Question 25

Support vector machine (SVM)

Answer 25

Classification algorithm that uses a boundary to separate the data into
two or more categories (“classes”).

Question 26

Unsupervised learning

Answer 26

Graphical representation of splitting a plane with two or more special
points into regions with one special point each, where each region’s
points are closer to the region’s special point than to any other special
point.

Question 27

Accuracy

Answer 27

Fraction of data points correctly classified by a model; equal to
TP +TN / TP +FP+TN+FN

Question 28

Confusion matrix

Answer 28

Visualization of classification model performance

Question 29

Diagnostic odds ratio

Answer 29

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 (TP / FN) / (FP / TN) = (TN X TP) / (FN X FP)

Question 30

Fall out

Answer 30

fraction of data points not in a certain category that are incorrectly classified by a model; equal to fp /(TN + FP) also called false positive rate

Question 31

False negative (FN)

Answer 31

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 32

False negative rate

Answer 32

Fraction of data points in a certain category that are incorrectly
classified by a model; equal to FN/TP+FN. Also called miss rate

Question 33

False positive (FP)

Answer 33

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 34

False positive rate

Answer 34

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

Question 35

False omission rate

Answer 35

Fraction of data points the model classifies as not in a certain category, that are really in the category; equal to NF/(TN+FN)

Question 36

Hit rate

Answer 36

Fraction of data points in a certain category that are correctly classified by a model; equal to TP/(TP+FN) sensitivity, and recall.

Question 37

Miss rate

Answer 37

Fraction of data points in a certain category that are incorrectly classified by a model; equal to FN/(TP+FN) Also called false negative rate

Question 38

Negative likelihood ratio

Answer 38

Ratio of the fraction of data points in a certain category that are misclassified as not in the cateogry, 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 = (FN/(FN+TP))/(TN/(TN+FP))

Question 39

Negative predictive value

Answer 39

Fraction of data points classified as not in a certain category that are really not in that category; equal to TP / (TP+FP)

Question 40

Positive likelihood ratio

Answer 40

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) = (TP / (TP+FN)) / (FP/(FP+TN)

Question 41

Positive predictive value

Answer 41

Fraction of data points classified as being in a certain category that are really in that category; equal to TP / (TP+FP) Precision . Also called precision

Question 42

Precision

Answer 42

In analytics, the fraction of data points classified as being in a certain category that are really in that category; equal to TP / (TP+FP) positive predictive value.

Question 43

Recall

Answer 43

Fraction of data points in a certain category that are correctly classified by a model; equal to TP / (TP + FN) positive rate.

Question 44

Sensitivity

Answer 44

Fraction of data points in a certain category that are correctly classified by a model; equal to TP/(TP+FN) and recall

Question 45

Specificity

Answer 45

Fraction of data points not in a certain category that are correctly classified by a model; equal to TN/(TN+FP) rate. also called the true negative rate

Question 46

True negative (TN)

Answer 46

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 47

True negative rate

Answer 47

Fraction of data points not in a certain category that are correctly classified by a model; equal to TN / (TN+FP) ; also called specificity.

Question 48

True positive (TP)

Answer 48

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 49

True positive rate

Answer 49

Fraction of data points in a certain category that are correctly classified by a model; equal to tp/ (TP+FN) ; also called sensitivity, hit rate, and recall.