DECISION TREE Ge Song. Introduction ■ Decision Tree: is a supervised learning algorithm used for...

4
DECISION TREE Ge Song

Transcript of DECISION TREE Ge Song. Introduction ■ Decision Tree: is a supervised learning algorithm used for...

Page 1: DECISION TREE Ge Song. Introduction ■ Decision Tree: is a supervised learning algorithm used for classification or regression. ■ Decision Tree Graph:

DECISION TREEGe Song

Page 2: DECISION TREE Ge Song. Introduction ■ Decision Tree: is a supervised learning algorithm used for classification or regression. ■ Decision Tree Graph:

Introduction■ Decision Tree: is a supervised learning algorithm used for classification or

regression.

■ Decision Tree Graph: is a graph that uses a branching method to illustrate every possible outcome of a decision.

input

output

Each internal node: test one discrete-valued attribute Xi Each branch from a node: selects one value for XiEach leaf node: predict Y (or P(Y|X leaf)) ∈

Page 3: DECISION TREE Ge Song. Introduction ■ Decision Tree: is a supervised learning algorithm used for classification or regression. ■ Decision Tree Graph:

ID3 Algorithm

■Calculate the entropy of every attribute using the data set

■Split the set into subsets using the attribute for which entropy is minimum (or, equivalently, information gain is maximum)

■Make a decision tree node containing that attribute

■Recurse on subsets using remaining attributes.

Page 4: DECISION TREE Ge Song. Introduction ■ Decision Tree: is a supervised learning algorithm used for classification or regression. ■ Decision Tree Graph:

Result and Limitations■ Result: [18+/13-]

H01420 = yes: [2+/12-]

| D12765 = yes: [2+/0-]

| D12765 = no: [0+/12-]

H01420 = no: [16+/1-]

| D14662 = yes: [0+/1-]

| D14662 = no: [16+/0-]

testError: 0.4

■ Limitation:

1) The rule (average max and min) that converts attribute values to binary values;

2) The threshold (0.1) that used to check whether to split or not ;

3) The depth of the tree and the robustness;

4) The cross validation step.


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