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Flashcards in Book - Chapter 7 Analytical Theory Classification Deck (36)
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1

What applications does classification appear in

Data mining

2

What is the primary task of a classifier

To assign class labels to new observations

3

Are classification method supervised or unsupervised

Supervised

4

What is another name for a decision tree

Prediction tree

5

What is the input variable of a decision tree

Categorical or continuous

6

In a decision tree structure what is a test point

A node

7

What is a node without further branches called

A leaf node

8

What do leaf nodes return

They return class labels and, in some implementations, they return the probability scores

9

What are the two varieties of decision trees

Classification trees and regression trees

10

What are classification trees

They usually apply to output variables that are categorical for example often binary yes or no

11

What are regression trees

They can apply to output variables that are numerical continuous, such as the predicted price of a consumer good or the likely heard a subscription will be purchased

12

What does the term branch mean in decision trees

Refers to the outcome of a decision and is visualised as a line connecting two Nodes

13

What happens if the decision is numerical

The greater than branch is usually placed on the right

14

What is an internal node

Are the dissertation or test points. Each internal note refers to an input variable or an attribute

15

What is the top internal node called

The root

16

What is the depth of a node

Is the minimum number of steps required to reach the node from the root

17

What are short trees also known as

Weak learners or base learners

18

What’s on in ensemble Mefford

They use multiple predictive models to vote, and decisions can be made based on the combination of the votes

19

Gave examples of ensemble methods

Random forest, bagging, and boasting

20

What is the simplest short tree called

Decision stump

21

At each split what does the decision tree algorithm do

It picks the most informative attribute out of the remaining attributes

22

How is the most informative attribute determined

By measures such as entropy and information gain

23

What does entropy measure

The impurity of an attribute

24

What does information gain measure

The purity of an attribute

25

When do you achieve maximum entropy

When all class labels are equally probable

26

What is conditional entropy always

Less than or equal to the base Entropy

27

What is information gain defined as

The difference between base Entropy and conditional entropy

28

What is Bayes theorem

Gives a relationship between the probabilities of two events and their conditional probabilities

29

What is a naive Bayes classifier

Assumes that the presence or absence of a particular feature of a class is unrelated to the presence or absence of other features

30

What are the input variables of naive Bayes

Categorical and I’ll discreet