MODULE 2 S3.1

Question 1

They are widely used for classification and regression tasks.

Answer 1

Decision Trees

Question 2

Decision trees learn a hierarchy of _________ questions, leading to a ____________.

Answer 2

if/else
decision

Question 3

Learning a decision tree means learning the sequence of if/else questions that gets us to the __________ answer most quickly.

Answer 3

true

Question 4

In the machine learning setting, questions are called _________

Answer 4

tests

Question 5

T/F To build a tree, the algorithm searches over all possible tests and finds the one that is most informative about the target variable

Answer 5

True

Question 6

The top node, which represents the whole dataset

Answer 6

root

Question 7

Decision tree classes

Answer 7

DecisionTreeRegressor
DecisionTreeClassifier

Question 8

We can visualize the tree using the ____________ function from the tree module.

Answer 8

export_graphiz

Question 9

It is a text file format for storing graphs.

Answer 9

.dot

Question 10

It is a diagram or chart that people use to determine a course of action or show a statistical probability.

Answer 10

Decision tree

Question 11

____________ : feature (attribute)
____________ : decision (rule) or reaction
____________ : outcome

Answer 11

node
branch
leaf

Question 12

Types of Decision Tree in Machine Learning

Answer 12

Classification trees
Regression trees

Question 13

Decision Variables

Classification : _____________
Regression : ______________

Answer 13

categorical
continuous

Question 14

The topmost node of a decision tree that represents the entire message or decision.

Answer 14

Root node

Question 15

The process of dividing a bode into two or more nodes. It’s the part at which the decision branches off into variables.

Answer 15

Splitting

Question 16

A node within a decision tree where the prior nose branches into two or more variables.

Answer 16

Decision (internal) node

Question 17

Also called as the external or terminal node, It is the last node in the tree and furthest from the root node.

Answer 17

Leaf (terminal) node

Question 18

Paths that connect the nodes and represent the different possible outcomes of the test.

Answer 18

Branch

Question 19

Nodes that precede other nodes in the tree hierarchy.

Answer 19

Parent node

Question 20

Nodes directly connected to the parent node, resulting from the split or decision made at the parent node.

Answer 20

Child node

Question 21

The opposite of splitting, the process of going through and reducing the tree to only the most important nodes or outcomes.

Answer 21

Pruning

Question 22

Decision Tree Algorithms

Answer 22

ID3 (Iterative Dichotomiser 3)
C4.5
CART (Classification and Regression Tree)
CHAID (Chi-square Automatic Interaction Detection)
MARS (Multivariate Adaptive Regression Splines)

Question 23

This algorithm uses the information gain metric to determine the best feature to split on at each node.

Answer 23

ID3 (Iterative Dichotomiser 3)

Question 24

T/F ID3 is prone to underfit.

Answer 24

False

Question 25

ID3 is prone to _________ and can create __________.

Answer 25

overfitting huge trees

Question 26

Algorithm that continues splitting until all instances are perfectly classified or no further useful features are available.

Answer 26

ID3

Question 27

ID3 was developed by _________________ in ______

Answer 27

Ross Quinlan 1986

Question 28

successor of ID3

Answer 28

C4.5

Question 29

Who developed C4.5?

Answer 29

Ross Quinlan

Question 30

Algorithm that uses the gain ratio metric instead of information gain to account for the number of branches in a feature.

Answer 30

C4.5

Question 31

It handles both categorixal and continuous data and prunes trees to avoid overfitting, which makes it better at handling noisy data.

Answer 31

C4.5

Question 32

Algorithm that can be used for classification and regression problems and uses Gini impurity or mean squared error.

Answer 32

CART (Classification and Regression Tree)

Question 33

CART was developed by

Answer 33

Leo Breiman Jerome Friedman Richard Olshen Charles Stone

Question 34

CART uses : classification : _____________ regression : ______________

Answer 34

Gini impurity mean squared error

Question 35

It provides clear and interpretable models, and trees are pruned to prevent overfitting.

Answer 35

CART

Question 36

Algorithm that uses chi-squared tests to find the best split.

Answer 36

CHAID (Chi-square Automatic Interaction Detection)

Question 37

Algorithm that is typically used for categorical variables and can handle multiway splits. It performs multi-level splits when computing classification trees.

Answer 37

CHAID

Question 38

CHAID was developed by ____________

Answer 38

Gordon Kass

Question 39

Algorithm that is primarily used for regression. It builds models by fitting piecewise linear regressions and combining them into a single model.

Answer 39

MARS (Multivariate Adaptive Regression Splines)

Question 40

T/F MARS is capable of modeling complex, nonlinear relationships, and interactions between featers.

Answer 40

True

Question 41

MARS was developed by ______________

Answer 41

Jerome Friedman

Question 42

Full form of ID3

Answer 42

Iterative Dichotomiser 3

Question 43

Full form of CART

Answer 43

Classification and Regression Tree

Question 44

Full form of CHAID

Answer 44

Chi-square Automatic Interaction Detection

Question 45

Full form of MARS

Answer 45

Multivariate Adaptive Regression Splines

Question 46

T/F CART is an n-ary tree

Answer 46

FALSE Binary tree