DECISION TREE Flashcards

1
Q

what is decision tree?

A

a decision tree is a non-parametric supervised learning algo for classification and regression tasks

it contains questions organized hierarchically in the shape of a tree

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2
Q

what are the questions usually called?

A

test, split and condition

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3
Q

what does a decision tree consists of?

A

root node
branches
internal nodes
leaf nodes
decision node
sub tree
parent and child node

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4
Q

what is a root node?

A

initial or the starting node of a decision tree from where the entire dataset starts dividing based on various conditions

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5
Q

what are decision node?

A

nodes resulting from the splitting of the root node

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6
Q

what are leaf node?

A

nodes where further splitting is not possible indicating the final classification or outcome

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7
Q

which nodes indicate the final outcome

A

leaf nodes

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8
Q

leaf nodes are referred as?

A

terminal node

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9
Q

what is a sub tree

A

a sub-section of a decision tree is referred to as a sub-tree. It represents a specific portion of the decision tree.

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10
Q

what is parent node

A

node that is divided into sub nodes

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11
Q

what is child node

A

nodes emerging from a parent node

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12
Q

what does parent node represent
?

A

a decision or a condition

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13
Q

what does a child node represent?

A

outcomes or further decision

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14
Q

what is pruning

A

the process of removing specific nodes from the decision tree to prevent overfitting and simplify the model.

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15
Q

what is inference path

A

inference of a decision tree is calculated from the root to one of the leaf nodes according to the conditions.

The value of the reached leaf is the tree’s prediction

the set of visited node is called inference path

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16
Q

types of condition?

A

axis aligned and oblique

17
Q

what is axis aligned condition

A

involves only a single feature

18
Q

what is oblique condition

A

involves multiple features

19
Q

which condition is more powerful?

A

oblique as it can express more complex patterns

20
Q

oblique gives better results because

A

at the expense of higher training and inference costs

21
Q
A
22
Q
A