L8decisiontree1

L8decisiontree1 - Classification Lecture Notes (3) (cse352)...

Info iconThis preview shows pages 1–9. Sign up to view the full content.

View Full Document Right Arrow Icon
Classification Lecture Notes (3) (cse352) DECISION TREE CLASSIFICATION (Supervised Learning) Professor Anita Wasilewska
Background image of page 1

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Classification Learning ALGORITHMS Different Classifiers • DESCRIPTIVE: • Decision Trees (ID3, C4.5) • Rough Sets • Genetic Algorithms • STATISTICAL: • Neural Networks • Bayesian Networks
Background image of page 2
Classification Data Data format: a data table with key attribute removed. Special attribute- class attribute must be distinguished age income student credit_rating buys_computer <=30 high no fair <=30 high excellent 31…40 high fair yes >40 medium fair yes >40 low yes fair yes >40 low yes excellent 31…40 low yes excellent yes <=30 medium fair <=30 low yes fair yes >40 medium yes fair yes <=30 medium yes excellent yes 31…40 medium excellent yes 31…40 high yes fair yes >40 medium excellent
Background image of page 3

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Classification (Training ) Data with objects rec Age Income Student Credit_rating Buys_computer (CLASS) r1 <=30 High No Fair No r2 <=30 High No Excellent No r3 31…40 High No Fair Yes r4 >40 Medium No Fair Yes r5 >40 Low Yes Fair Yes r6 >40 Low Yes Excellent No r7 31…40 Low Yes Excellent Yes r8 <=30 Medium No Fair No r9 <=30 Low Yes Fair Yes r10 >40 Medium Yes Fair Yes r11 <=30 Medium Yes Excellent Yes r12 31…40 Medium No Excellent Yes r13 31…40 High Yes Fair Yes r14 >40 Medium No Excellent No
Background image of page 4
Classification by Decision Tree Induction • Decision tree is A flow-chart-like tree structure; Internal node denotes an attribute; Branch represents the values of the node attribute; Leaf nodes represent class labels or class distribution
Background image of page 5

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
DECISION TREE An Example age student credit rating no yes fair excellent <=30 >40 31. .40 Buys=yes Buys=no Buys=no Buys=yes Buys=yes
Background image of page 6
Classification by Decision Tree Induction Decision tree generation consists of two phases Tree construction We choose recursively internal nodes (attributes) with their proper values as branches. At start we choose one attribute as the root and put all its values as branches We Stop when all the samples (records) are of the same class, then the node becomes the leaf labeled with that class or there is no more samples (records) left. In this case we apply MAJORITY VOTING to classify the node. Majority Voting involves converting the node into a leaf and labeling it with the most common class in the training set. Some algorithms allow majority voting at any level of the tree, i.e. converting a given node into a leaf and labeling it with the most common class at the node. We call it General Majority Voting . Tree pruning Identify and remove branches that reflect noise or outliers
Background image of page 7

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Classification by Decision Tree Induction Crucial point Good choice of the root attribute and internal nodes attributes is a crucial point. Bad choice may result, in the worst case in a just another knowledge representation: relational table re-written as a tree with class attributes (decision attributes) as the leaves.
Background image of page 8
Image of page 9
This is the end of the preview. Sign up to access the rest of the document.

This note was uploaded on 01/25/2012 for the course CSE 352 taught by Professor Wasilewska,a during the Fall '08 term at SUNY Stony Brook.

Page1 / 68

L8decisiontree1 - Classification Lecture Notes (3) (cse352)...

This preview shows document pages 1 - 9. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online