Artificial Intelligence. Topic

Size: px
Start display at page:

Download "Artificial Intelligence. Topic"

Transcription

1 Artificial Intelligence Topic

2 What is decision tree? A tree where each branching node represents a choice between two or more alternatives, with every branching node being part of a path to a leaf node (bottom of the tree). The leaf node represents a decision, derived from the tree for the given input.

3

4

5 ID3 Algorithm Top-down, greedy search No backtracking: So, algorithm proceeds from top to bottom

6 What is a greedy search? At each step, make decision which makes greatest improvement in whatever you are trying optimize. Do not backtrack (unless you hit a dead end) This type of search is likely not to be a globally optimum solution, but generally works well. What are we really doing here? At each node of tree, make decision on which attribute best classifies training data at that point. Never backtrack (in ID3) Do this for each branch of tree. End result will be tree structure representing a hypothesis which works best for the training data.

7 Constructing a decision tree using information gain A decision tree can be constructed top-down using the information gain in the following way: begin at the root node determine the attribute with the highest information gain which is not already used as an ancestor node add a child node for each possible value of that attribute attach all examples to the child node where the attribute values of the examples are identical to the attribute value attached to the node if all examples attached to the child node can be classified uniquely add that classification to that node and mark it as leaf node go back to step two if there are unused attributes left, otherwise add the classification of most of the examples attached to the child node.

8

9

10

11 Object, sample, example Training Examples Attribute, variable, property Shall we play tennis today? (Tennis 1) decision

12 Shall we play tennis today?

13 The tree itself forms hypothesis Disjunction (OR s) of conjunctions (AND s) Each path from root to leaf forms conjunction of constraints on attributes Separate branches are disjunctions Example from Play Tennis decision tree: (Outlook=Sunny Humidity=Normal) (Outlook=Overcast) (Outlook=Rain Wind=Weak)

14 Hypothesis space can include disjunctive expressions. Set of possible decision trees. In fact, hypothesis space is complete space of finite discrete-valued functions

15 Question? How do you determine which attribute best classifies data?

16

17 What is entropy? Entropy is a measure of the uncertainty associated with a random variable

18 e.g. A series of coin tosses with a fair coin has maximum entropy, since there is no way to predict what will come next. A string of coin tosses with a coin with two heads and no tails has zero entropy, since the coin will always come up heads. A single toss of a fair coin has an entropy of one bit, but a particular result (e.g. "heads") has zero entropy, since it is entirely "predictable

19 Entropy Entropy (disorder, impurity) of a set of examples, S, relative to a binary classification is: Entropy H ( S) p1 log2( p1) p0 log2( p0) where p 1 is the fraction of positive examples in S and p 0 is the fraction of negatives. If all examples are in one category, entropy is zero (we define 0 log(0)=0) If examples are equally mixed (p 1 =p 0 =0.5), entropy is a maximum of 1. 19

20 Information gain Information gain is how well one attribute classifies the training data.in other words it is the reduction in entropy. Mathematical expression for information gain: A i Gain( S, A ) H( S) P( A v) H( S ) i i v v Values ( A i )

21 ID3 algorithm (for boolean-valued function) Calculate the entropy for all training examples positive and negative cases p + = #pos/tot p - = #neg/tot H(S) = -p + log 2 (p + ) - p - log 2 (p - ) Use attribute with greatest information gain as a root

22 Example: PlayTennis Four attributes used for classification: Outlook = {Sunny,Overcast,Rain} Temperature = {Hot, Mild, Cool} Humidity = {High, Normal} Wind = {Weak, Strong} One predicted (target) attribute (binary) PlayTennis = {Yes,No} Given 14 Training examples 9 positive 5 negative

23 Examples, minterms, cases, objects, test cases, Training Examples

24 14 cases 9 positive cases Step 1: Calculate entropy for all cases: entropy N Pos = 9 N Neg = 5 N Tot = 14 H(S) = -(9/14)*log 2 (9/14) - (5/14)*log 2 (5/14) = 0.940

25 Step 2: Loop over all attributes, calculate gain: Attribute = Outlook Loop over values of Outlook Outlook = Sunny N Pos = 2 N Neg = 3 N Tot = 5 H(Sunny) = -(2/5)*log 2 (2/5) - (3/5)*log 2 (3/5) = Outlook = Overcast N Pos = 4 N Neg = 0 N Tot = 4 H(Overcast) = -(4/4)*log 2 4/4) - (0/4)*log 2 (0/4) = 0.00

26 Outlook = Rain N Pos = 3 N Neg = 2 N Tot = 5 H(Rain) = -(3/5)*log 2 (3/5) - (2/5)*log 2 (2/5) = Calculate Information Gain for attribute Outlook Gain(S,Outlook) = H(S) - N Sunny /N Tot *H(Sunny) - N Over /N Tot *H(Overcast) - N Rain /N Tot *H(Rainy) Gain(S,Outlook) = (5/14)* (4/14)*0 - (5/14)*0.971 Gain(S,Outlook) = Attribute = Temperature (Repeat process looping over {Hot, Mild, Cool}) Gain(S,Temperature) = 0.029

27 Attribute = Humidity (Repeat process looping over {High, Normal}) Gain(S,Humidity) = Attribute = Wind (Repeat process looping over {Weak, Strong}) Gain(S,Wind) = Find attribute with greatest information gain: Gain(S,Outlook) = 0.246, Gain(S,Temperature) = Gain(S,Humidity) = 0.029, Gain(S,Wind) = Outlook is root node of tree

28 Iterate algorithm to find attributes which best classify training examples under the values of the root node Example continued Take three subsets: Outlook = Sunny (N Tot = 5) Outlook = Overcast (N Tot = 4) Outlook = Rainy (N Tot = 5) For each subset, repeat the above calculation looping over all attributes other than Outlook

29 For example: Outlook = Sunny (N Pos = 2, N Neg =3, N Tot = 5) H=0.971 Temp = Hot (N Pos = 0, N Neg =2, N Tot = 2) H = 0.0 Temp = Mild (N Pos = 1, N Neg =1, N Tot = 2) H = 1.0 Temp = Cool (N Pos = 1, N Neg =0, N Tot = 1) H = 0.0 Gain(S Sunny,Temperature) = (2/5)*0 - (2/5)*1 - (1/5)*0 Gain(S Sunny,Temperature) = Similarly: Gain(S Sunny,Humidity) = Gain(S Sunny,Wind) = Humidity classifies Outlook=Sunny instances best and is placed as the node under Sunny outcome. Repeat this process for Outlook = Overcast &Rainy

30 End up with tree:

31 Drawback A drawback of using decision trees is that the outcomes of decisions, subsequent decisions and payoffs may be based primarily on expectations. For example, if you expect that your parents will pay for half of your college when deciding to go to school, but later discover that you will have to pay for all of your tuition, your expected payoffs will be dramatically different than reality.

32

33 Controlling Over fitting Introduce a restriction on the hypotheses space to prevent overly-complex hypotheses from being learned.

34

35 Pros and Cons of Decision Tree Advantages: Works well with discrete data Produces human-comprehensible concepts Easy to understand Able to process both numerical and categorical data Disadvantages: Trees created from numeric datasets can be complex Limited to one output attribute

36 THANKS

Learning Classification Trees. Sargur Srihari

Learning Classification Trees. Sargur Srihari Learning Classification Trees Sargur srihari@cedar.buffalo.edu 1 Topics in CART CART as an adaptive basis function model Classification and Regression Tree Basics Growing a Tree 2 A Classification Tree

More information

Decision Tree Learning and Inductive Inference

Decision Tree Learning and Inductive Inference Decision Tree Learning and Inductive Inference 1 Widely used method for inductive inference Inductive Inference Hypothesis: Any hypothesis found to approximate the target function well over a sufficiently

More information

Learning Decision Trees

Learning Decision Trees Learning Decision Trees Machine Learning Fall 2018 Some slides from Tom Mitchell, Dan Roth and others 1 Key issues in machine learning Modeling How to formulate your problem as a machine learning problem?

More information

Learning Decision Trees

Learning Decision Trees Learning Decision Trees Machine Learning Spring 2018 1 This lecture: Learning Decision Trees 1. Representation: What are decision trees? 2. Algorithm: Learning decision trees The ID3 algorithm: A greedy

More information

Introduction. Decision Tree Learning. Outline. Decision Tree 9/7/2017. Decision Tree Definition

Introduction. Decision Tree Learning. Outline. Decision Tree 9/7/2017. Decision Tree Definition Introduction Decision Tree Learning Practical methods for inductive inference Approximating discrete-valued functions Robust to noisy data and capable of learning disjunctive expression ID3 earch a completely

More information

Imagine we ve got a set of data containing several types, or classes. E.g. information about customers, and class=whether or not they buy anything.

Imagine we ve got a set of data containing several types, or classes. E.g. information about customers, and class=whether or not they buy anything. Decision Trees Defining the Task Imagine we ve got a set of data containing several types, or classes. E.g. information about customers, and class=whether or not they buy anything. Can we predict, i.e

More information

Decision Trees / NLP Introduction

Decision Trees / NLP Introduction Decision Trees / NLP Introduction Dr. Kevin Koidl School of Computer Science and Statistic Trinity College Dublin ADAPT Research Centre The ADAPT Centre is funded under the SFI Research Centres Programme

More information

Decision Tree Learning - ID3

Decision Tree Learning - ID3 Decision Tree Learning - ID3 n Decision tree examples n ID3 algorithm n Occam Razor n Top-Down Induction in Decision Trees n Information Theory n gain from property 1 Training Examples Day Outlook Temp.

More information

Decision Trees. Tirgul 5

Decision Trees. Tirgul 5 Decision Trees Tirgul 5 Using Decision Trees It could be difficult to decide which pet is right for you. We ll find a nice algorithm to help us decide what to choose without having to think about it. 2

More information

Dan Roth 461C, 3401 Walnut

Dan Roth   461C, 3401 Walnut CIS 519/419 Applied Machine Learning www.seas.upenn.edu/~cis519 Dan Roth danroth@seas.upenn.edu http://www.cis.upenn.edu/~danroth/ 461C, 3401 Walnut Slides were created by Dan Roth (for CIS519/419 at Penn

More information

Decision Tree Learning Mitchell, Chapter 3. CptS 570 Machine Learning School of EECS Washington State University

Decision Tree Learning Mitchell, Chapter 3. CptS 570 Machine Learning School of EECS Washington State University Decision Tree Learning Mitchell, Chapter 3 CptS 570 Machine Learning School of EECS Washington State University Outline Decision tree representation ID3 learning algorithm Entropy and information gain

More information

Decision Tree Learning

Decision Tree Learning Topics Decision Tree Learning Sattiraju Prabhakar CS898O: DTL Wichita State University What are decision trees? How do we use them? New Learning Task ID3 Algorithm Weka Demo C4.5 Algorithm Weka Demo Implementation

More information

Typical Supervised Learning Problem Setting

Typical Supervised Learning Problem Setting Typical Supervised Learning Problem Setting Given a set (database) of observations Each observation (x1,, xn, y) Xi are input variables Y is a particular output Build a model to predict y = f(x1,, xn)

More information

Machine Learning Recitation 8 Oct 21, Oznur Tastan

Machine Learning Recitation 8 Oct 21, Oznur Tastan Machine Learning 10601 Recitation 8 Oct 21, 2009 Oznur Tastan Outline Tree representation Brief information theory Learning decision trees Bagging Random forests Decision trees Non linear classifier Easy

More information

Machine Learning 2nd Edi7on

Machine Learning 2nd Edi7on Lecture Slides for INTRODUCTION TO Machine Learning 2nd Edi7on CHAPTER 9: Decision Trees ETHEM ALPAYDIN The MIT Press, 2010 Edited and expanded for CS 4641 by Chris Simpkins alpaydin@boun.edu.tr h1p://www.cmpe.boun.edu.tr/~ethem/i2ml2e

More information

ARTIFICIAL INTELLIGENCE. Supervised learning: classification

ARTIFICIAL INTELLIGENCE. Supervised learning: classification INFOB2KI 2017-2018 Utrecht University The Netherlands ARTIFICIAL INTELLIGENCE Supervised learning: classification Lecturer: Silja Renooij These slides are part of the INFOB2KI Course Notes available from

More information

Decision Trees. Danushka Bollegala

Decision Trees. Danushka Bollegala Decision Trees Danushka Bollegala Rule-based Classifiers In rule-based learning, the idea is to learn a rule from train data in the form IF X THEN Y (or a combination of nested conditions) that explains

More information

Lecture 3: Decision Trees

Lecture 3: Decision Trees Lecture 3: Decision Trees Cognitive Systems II - Machine Learning SS 2005 Part I: Basic Approaches of Concept Learning ID3, Information Gain, Overfitting, Pruning Lecture 3: Decision Trees p. Decision

More information

Rule Generation using Decision Trees

Rule Generation using Decision Trees Rule Generation using Decision Trees Dr. Rajni Jain 1. Introduction A DT is a classification scheme which generates a tree and a set of rules, representing the model of different classes, from a given

More information

CS 6375 Machine Learning

CS 6375 Machine Learning CS 6375 Machine Learning Decision Trees Instructor: Yang Liu 1 Supervised Classifier X 1 X 2. X M Ref class label 2 1 Three variables: Attribute 1: Hair = {blond, dark} Attribute 2: Height = {tall, short}

More information

Decision Tree Learning

Decision Tree Learning Topics Decision Tree Learning Sattiraju Prabhakar CS898O: DTL Wichita State University What are decision trees? How do we use them? New Learning Task ID3 Algorithm Weka Demo C4.5 Algorithm Weka Demo Implementation

More information

Decision Trees. Data Science: Jordan Boyd-Graber University of Maryland MARCH 11, Data Science: Jordan Boyd-Graber UMD Decision Trees 1 / 1

Decision Trees. Data Science: Jordan Boyd-Graber University of Maryland MARCH 11, Data Science: Jordan Boyd-Graber UMD Decision Trees 1 / 1 Decision Trees Data Science: Jordan Boyd-Graber University of Maryland MARCH 11, 2018 Data Science: Jordan Boyd-Graber UMD Decision Trees 1 / 1 Roadmap Classification: machines labeling data for us Last

More information

Decision Trees. Gavin Brown

Decision Trees. Gavin Brown Decision Trees Gavin Brown Every Learning Method has Limitations Linear model? KNN? SVM? Explain your decisions Sometimes we need interpretable results from our techniques. How do you explain the above

More information

Decision-Tree Learning. Chapter 3: Decision Tree Learning. Classification Learning. Decision Tree for PlayTennis

Decision-Tree Learning. Chapter 3: Decision Tree Learning. Classification Learning. Decision Tree for PlayTennis Decision-Tree Learning Chapter 3: Decision Tree Learning CS 536: Machine Learning Littman (Wu, TA) [read Chapter 3] [some of Chapter 2 might help ] [recommended exercises 3.1, 3.2] Decision tree representation

More information

Lecture 3: Decision Trees

Lecture 3: Decision Trees Lecture 3: Decision Trees Cognitive Systems - Machine Learning Part I: Basic Approaches of Concept Learning ID3, Information Gain, Overfitting, Pruning last change November 26, 2014 Ute Schmid (CogSys,

More information

Classification Using Decision Trees

Classification Using Decision Trees Classification Using Decision Trees 1. Introduction Data mining term is mainly used for the specific set of six activities namely Classification, Estimation, Prediction, Affinity grouping or Association

More information

The Naïve Bayes Classifier. Machine Learning Fall 2017

The Naïve Bayes Classifier. Machine Learning Fall 2017 The Naïve Bayes Classifier Machine Learning Fall 2017 1 Today s lecture The naïve Bayes Classifier Learning the naïve Bayes Classifier Practical concerns 2 Today s lecture The naïve Bayes Classifier Learning

More information

Decision Trees Part 1. Rao Vemuri University of California, Davis

Decision Trees Part 1. Rao Vemuri University of California, Davis Decision Trees Part 1 Rao Vemuri University of California, Davis Overview What is a Decision Tree Sample Decision Trees How to Construct a Decision Tree Problems with Decision Trees Classification Vs Regression

More information

Chapter 3: Decision Tree Learning

Chapter 3: Decision Tree Learning Chapter 3: Decision Tree Learning CS 536: Machine Learning Littman (Wu, TA) Administration Books? New web page: http://www.cs.rutgers.edu/~mlittman/courses/ml03/ schedule lecture notes assignment info.

More information

Decision Tree Learning

Decision Tree Learning Decision Tree Learning Berlin Chen Department of Computer Science & Information Engineering National Taiwan Normal University References: 1. Machine Learning, Chapter 3 2. Data Mining: Concepts, Models,

More information

CS6375: Machine Learning Gautam Kunapuli. Decision Trees

CS6375: Machine Learning Gautam Kunapuli. Decision Trees Gautam Kunapuli Example: Restaurant Recommendation Example: Develop a model to recommend restaurants to users depending on their past dining experiences. Here, the features are cost (x ) and the user s

More information

Question of the Day. Machine Learning 2D1431. Decision Tree for PlayTennis. Outline. Lecture 4: Decision Tree Learning

Question of the Day. Machine Learning 2D1431. Decision Tree for PlayTennis. Outline. Lecture 4: Decision Tree Learning Question of the Day Machine Learning 2D1431 How can you make the following equation true by drawing only one straight line? 5 + 5 + 5 = 550 Lecture 4: Decision Tree Learning Outline Decision Tree for PlayTennis

More information

Supervised Learning! Algorithm Implementations! Inferring Rudimentary Rules and Decision Trees!

Supervised Learning! Algorithm Implementations! Inferring Rudimentary Rules and Decision Trees! Supervised Learning! Algorithm Implementations! Inferring Rudimentary Rules and Decision Trees! Summary! Input Knowledge representation! Preparing data for learning! Input: Concept, Instances, Attributes"

More information

Classification and regression trees

Classification and regression trees Classification and regression trees Pierre Geurts p.geurts@ulg.ac.be Last update: 23/09/2015 1 Outline Supervised learning Decision tree representation Decision tree learning Extensions Regression trees

More information

Introduction to ML. Two examples of Learners: Naïve Bayesian Classifiers Decision Trees

Introduction to ML. Two examples of Learners: Naïve Bayesian Classifiers Decision Trees Introduction to ML Two examples of Learners: Naïve Bayesian Classifiers Decision Trees Why Bayesian learning? Probabilistic learning: Calculate explicit probabilities for hypothesis, among the most practical

More information

Machine Learning in Bioinformatics

Machine Learning in Bioinformatics Machine Learning in Bioinformatics Arlindo Oliveira aml@inesc-id.pt Data Mining: Concepts and Techniques Data mining concepts Learning from examples Decision trees Neural Networks 1 Typical Supervised

More information

the tree till a class assignment is reached

the tree till a class assignment is reached Decision Trees Decision Tree for Playing Tennis Prediction is done by sending the example down Prediction is done by sending the example down the tree till a class assignment is reached Definitions Internal

More information

Decision trees. Special Course in Computer and Information Science II. Adam Gyenge Helsinki University of Technology

Decision trees. Special Course in Computer and Information Science II. Adam Gyenge Helsinki University of Technology Decision trees Special Course in Computer and Information Science II Adam Gyenge Helsinki University of Technology 6.2.2008 Introduction Outline: Definition of decision trees ID3 Pruning methods Bibliography:

More information

EECS 349:Machine Learning Bryan Pardo

EECS 349:Machine Learning Bryan Pardo EECS 349:Machine Learning Bryan Pardo Topic 2: Decision Trees (Includes content provided by: Russel & Norvig, D. Downie, P. Domingos) 1 General Learning Task There is a set of possible examples Each example

More information

Data classification (II)

Data classification (II) Lecture 4: Data classification (II) Data Mining - Lecture 4 (2016) 1 Outline Decision trees Choice of the splitting attribute ID3 C4.5 Classification rules Covering algorithms Naïve Bayes Classification

More information

Administration. Chapter 3: Decision Tree Learning (part 2) Measuring Entropy. Entropy Function

Administration. Chapter 3: Decision Tree Learning (part 2) Measuring Entropy. Entropy Function Administration Chapter 3: Decision Tree Learning (part 2) Book on reserve in the math library. Questions? CS 536: Machine Learning Littman (Wu, TA) Measuring Entropy Entropy Function S is a sample of training

More information

Decision Trees.

Decision Trees. . Machine Learning Decision Trees Prof. Dr. Martin Riedmiller AG Maschinelles Lernen und Natürlichsprachliche Systeme Institut für Informatik Technische Fakultät Albert-Ludwigs-Universität Freiburg riedmiller@informatik.uni-freiburg.de

More information

Decision Trees. Common applications: Health diagnosis systems Bank credit analysis

Decision Trees. Common applications: Health diagnosis systems Bank credit analysis Decision Trees Rodrigo Fernandes de Mello Invited Professor at Télécom ParisTech Associate Professor at Universidade de São Paulo, ICMC, Brazil http://www.icmc.usp.br/~mello mello@icmc.usp.br Decision

More information

Decision Tree Learning

Decision Tree Learning 0. Decision Tree Learning Based on Machine Learning, T. Mitchell, McGRAW Hill, 1997, ch. 3 Acknowledgement: The present slides are an adaptation of slides drawn by T. Mitchell PLAN 1. Concept learning:

More information

( D) I(2,3) I(4,0) I(3,2) weighted avg. of entropies

( D) I(2,3) I(4,0) I(3,2) weighted avg. of entropies Decision Tree Induction using Information Gain Let I(x,y) as the entropy in a dataset with x number of class 1(i.e., play ) and y number of class (i.e., don t play outcomes. The entropy at the root, i.e.,

More information

Bayesian Classification. Bayesian Classification: Why?

Bayesian Classification. Bayesian Classification: Why? Bayesian Classification http://css.engineering.uiowa.edu/~comp/ Bayesian Classification: Why? Probabilistic learning: Computation of explicit probabilities for hypothesis, among the most practical approaches

More information

CS 380: ARTIFICIAL INTELLIGENCE MACHINE LEARNING. Santiago Ontañón

CS 380: ARTIFICIAL INTELLIGENCE MACHINE LEARNING. Santiago Ontañón CS 380: ARTIFICIAL INTELLIGENCE MACHINE LEARNING Santiago Ontañón so367@drexel.edu Summary so far: Rational Agents Problem Solving Systematic Search: Uninformed Informed Local Search Adversarial Search

More information

Universität Potsdam Institut für Informatik Lehrstuhl Maschinelles Lernen. Intelligent Data Analysis. Decision Trees

Universität Potsdam Institut für Informatik Lehrstuhl Maschinelles Lernen. Intelligent Data Analysis. Decision Trees Universität Potsdam Institut für Informatik Lehrstuhl Maschinelles Lernen Intelligent Data Analysis Decision Trees Paul Prasse, Niels Landwehr, Tobias Scheffer Decision Trees One of many applications:

More information

Machine Learning 3. week

Machine Learning 3. week Machine Learning 3. week Entropy Decision Trees ID3 C4.5 Classification and Regression Trees (CART) 1 What is Decision Tree As a short description, decision tree is a data classification procedure which

More information

Decision Trees.

Decision Trees. . Machine Learning Decision Trees Prof. Dr. Martin Riedmiller AG Maschinelles Lernen und Natürlichsprachliche Systeme Institut für Informatik Technische Fakultät Albert-Ludwigs-Universität Freiburg riedmiller@informatik.uni-freiburg.de

More information

DECISION TREE LEARNING. [read Chapter 3] [recommended exercises 3.1, 3.4]

DECISION TREE LEARNING. [read Chapter 3] [recommended exercises 3.1, 3.4] 1 DECISION TREE LEARNING [read Chapter 3] [recommended exercises 3.1, 3.4] Decision tree representation ID3 learning algorithm Entropy, Information gain Overfitting Decision Tree 2 Representation: Tree-structured

More information

Bayesian Learning. Artificial Intelligence Programming. 15-0: Learning vs. Deduction

Bayesian Learning. Artificial Intelligence Programming. 15-0: Learning vs. Deduction 15-0: Learning vs. Deduction Artificial Intelligence Programming Bayesian Learning Chris Brooks Department of Computer Science University of San Francisco So far, we ve seen two types of reasoning: Deductive

More information

The Quadratic Entropy Approach to Implement the Id3 Decision Tree Algorithm

The Quadratic Entropy Approach to Implement the Id3 Decision Tree Algorithm Journal of Computer Science and Information Technology December 2018, Vol. 6, No. 2, pp. 23-29 ISSN: 2334-2366 (Print), 2334-2374 (Online) Copyright The Author(s). All Rights Reserved. Published by American

More information

Administrative notes. Computational Thinking ct.cs.ubc.ca

Administrative notes. Computational Thinking ct.cs.ubc.ca Administrative notes Labs this week: project time. Remember, you need to pass the project in order to pass the course! (See course syllabus.) Clicker grades should be on-line now Administrative notes March

More information

M chi h n i e n L e L arni n n i g Decision Trees Mac a h c i h n i e n e L e L a e r a ni n ng

M chi h n i e n L e L arni n n i g Decision Trees Mac a h c i h n i e n e L e L a e r a ni n ng 1 Decision Trees 2 Instances Describable by Attribute-Value Pairs Target Function Is Discrete Valued Disjunctive Hypothesis May Be Required Possibly Noisy Training Data Examples Equipment or medical diagnosis

More information

Outline. Training Examples for EnjoySport. 2 lecture slides for textbook Machine Learning, c Tom M. Mitchell, McGraw Hill, 1997

Outline. Training Examples for EnjoySport. 2 lecture slides for textbook Machine Learning, c Tom M. Mitchell, McGraw Hill, 1997 Outline Training Examples for EnjoySport Learning from examples General-to-specific ordering over hypotheses [read Chapter 2] [suggested exercises 2.2, 2.3, 2.4, 2.6] Version spaces and candidate elimination

More information

Classification and Prediction

Classification and Prediction Classification Classification and Prediction Classification: predict categorical class labels Build a model for a set of classes/concepts Classify loan applications (approve/decline) Prediction: model

More information

Classification and Regression Trees

Classification and Regression Trees Classification and Regression Trees Ryan P Adams So far, we have primarily examined linear classifiers and regressors, and considered several different ways to train them When we ve found the linearity

More information

Leveraging Randomness in Structure to Enable Efficient Distributed Data Analytics

Leveraging Randomness in Structure to Enable Efficient Distributed Data Analytics Leveraging Randomness in Structure to Enable Efficient Distributed Data Analytics Jaideep Vaidya (jsvaidya@rbs.rutgers.edu) Joint work with Basit Shafiq, Wei Fan, Danish Mehmood, and David Lorenzi Distributed

More information

Bayesian Learning. Reading: Tom Mitchell, Generative and discriminative classifiers: Naive Bayes and logistic regression, Sections 1-2.

Bayesian Learning. Reading: Tom Mitchell, Generative and discriminative classifiers: Naive Bayes and logistic regression, Sections 1-2. Bayesian Learning Reading: Tom Mitchell, Generative and discriminative classifiers: Naive Bayes and logistic regression, Sections 1-2. (Linked from class website) Conditional Probability Probability of

More information

Decision Trees. Each internal node : an attribute Branch: Outcome of the test Leaf node or terminal node: class label.

Decision Trees. Each internal node : an attribute Branch: Outcome of the test Leaf node or terminal node: class label. Decision Trees Supervised approach Used for Classification (Categorical values) or regression (continuous values). The learning of decision trees is from class-labeled training tuples. Flowchart like structure.

More information

Decision Tree And Random Forest

Decision Tree And Random Forest Decision Tree And Random Forest Dr. Ammar Mohammed Associate Professor of Computer Science ISSR, Cairo University PhD of CS ( Uni. Koblenz-Landau, Germany) Spring 2019 Contact: mailto: Ammar@cu.edu.eg

More information

Decision Support. Dr. Johan Hagelbäck.

Decision Support. Dr. Johan Hagelbäck. Decision Support Dr. Johan Hagelbäck johan.hagelback@lnu.se http://aiguy.org Decision Support One of the earliest AI problems was decision support The first solution to this problem was expert systems

More information

Symbolic methods in TC: Decision Trees

Symbolic methods in TC: Decision Trees Symbolic methods in TC: Decision Trees ML for NLP Lecturer: Kevin Koidl Assist. Lecturer Alfredo Maldonado https://www.cs.tcd.ie/kevin.koidl/cs0/ kevin.koidl@scss.tcd.ie, maldonaa@tcd.ie 01-017 A symbolic

More information

Machine Learning Alternatives to Manual Knowledge Acquisition

Machine Learning Alternatives to Manual Knowledge Acquisition Machine Learning Alternatives to Manual Knowledge Acquisition Interactive programs which elicit knowledge from the expert during the course of a conversation at the terminal. Programs which learn by scanning

More information

Machine Learning & Data Mining

Machine Learning & Data Mining Group M L D Machine Learning M & Data Mining Chapter 7 Decision Trees Xin-Shun Xu @ SDU School of Computer Science and Technology, Shandong University Top 10 Algorithm in DM #1: C4.5 #2: K-Means #3: SVM

More information

UVA CS 4501: Machine Learning

UVA CS 4501: Machine Learning UVA CS 4501: Machine Learning Lecture 21: Decision Tree / Random Forest / Ensemble Dr. Yanjun Qi University of Virginia Department of Computer Science Where are we? è Five major sections of this course

More information

Decision Tree Analysis for Classification Problems. Entscheidungsunterstützungssysteme SS 18

Decision Tree Analysis for Classification Problems. Entscheidungsunterstützungssysteme SS 18 Decision Tree Analysis for Classification Problems Entscheidungsunterstützungssysteme SS 18 Supervised segmentation An intuitive way of thinking about extracting patterns from data in a supervised manner

More information

Artificial Intelligence Decision Trees

Artificial Intelligence Decision Trees Artificial Intelligence Decision Trees Andrea Torsello Decision Trees Complex decisions can often be expressed in terms of a series of questions: What to do this Weekend? If my parents are visiting We

More information

Lecture 24: Other (Non-linear) Classifiers: Decision Tree Learning, Boosting, and Support Vector Classification Instructor: Prof. Ganesh Ramakrishnan

Lecture 24: Other (Non-linear) Classifiers: Decision Tree Learning, Boosting, and Support Vector Classification Instructor: Prof. Ganesh Ramakrishnan Lecture 24: Other (Non-linear) Classifiers: Decision Tree Learning, Boosting, and Support Vector Classification Instructor: Prof Ganesh Ramakrishnan October 20, 2016 1 / 25 Decision Trees: Cascade of step

More information

http://xkcd.com/1570/ Strategy: Top Down Recursive divide-and-conquer fashion First: Select attribute for root node Create branch for each possible attribute value Then: Split

More information

Classification: Decision Trees

Classification: Decision Trees Classification: Decision Trees Outline Top-Down Decision Tree Construction Choosing the Splitting Attribute Information Gain and Gain Ratio 2 DECISION TREE An internal node is a test on an attribute. A

More information

10-701/ Machine Learning: Assignment 1

10-701/ Machine Learning: Assignment 1 10-701/15-781 Machine Learning: Assignment 1 The assignment is due September 27, 2005 at the beginning of class. Write your name in the top right-hand corner of each page submitted. No paperclips, folders,

More information

Chapter 6: Classification

Chapter 6: Classification Chapter 6: Classification 1) Introduction Classification problem, evaluation of classifiers, prediction 2) Bayesian Classifiers Bayes classifier, naive Bayes classifier, applications 3) Linear discriminant

More information

Induction on Decision Trees

Induction on Decision Trees Séance «IDT» de l'ue «apprentissage automatique» Bruno Bouzy bruno.bouzy@parisdescartes.fr www.mi.parisdescartes.fr/~bouzy Outline Induction task ID3 Entropy (disorder) minimization Noise Unknown attribute

More information

Machine Learning and Data Mining. Decision Trees. Prof. Alexander Ihler

Machine Learning and Data Mining. Decision Trees. Prof. Alexander Ihler + Machine Learning and Data Mining Decision Trees Prof. Alexander Ihler Decision trees Func-onal form f(x;µ): nested if-then-else statements Discrete features: fully expressive (any func-on) Structure:

More information

The Solution to Assignment 6

The Solution to Assignment 6 The Solution to Assignment 6 Problem 1: Use the 2-fold cross-validation to evaluate the Decision Tree Model for trees up to 2 levels deep (that is, the maximum path length from the root to the leaves is

More information

Tutorial 6. By:Aashmeet Kalra

Tutorial 6. By:Aashmeet Kalra Tutorial 6 By:Aashmeet Kalra AGENDA Candidate Elimination Algorithm Example Demo of Candidate Elimination Algorithm Decision Trees Example Demo of Decision Trees Concept and Concept Learning A Concept

More information

Data Mining in Bioinformatics

Data Mining in Bioinformatics Data Mining in Bioinformatics Arlindo Oliveira aml@inesc-id.pt Data Mining: Concepts and Techniques Data mining concepts Learning from examples Decision trees Neural networks Clustering 1 Typical Supervised

More information

Apprentissage automatique et fouille de données (part 2)

Apprentissage automatique et fouille de données (part 2) Apprentissage automatique et fouille de données (part 2) Telecom Saint-Etienne Elisa Fromont (basé sur les cours d Hendrik Blockeel et de Tom Mitchell) 1 Induction of decision trees : outline (adapted

More information

Decision Trees. CSC411/2515: Machine Learning and Data Mining, Winter 2018 Luke Zettlemoyer, Carlos Guestrin, and Andrew Moore

Decision Trees. CSC411/2515: Machine Learning and Data Mining, Winter 2018 Luke Zettlemoyer, Carlos Guestrin, and Andrew Moore Decision Trees Claude Monet, The Mulberry Tree Slides from Pedro Domingos, CSC411/2515: Machine Learning and Data Mining, Winter 2018 Luke Zettlemoyer, Carlos Guestrin, and Andrew Moore Michael Guerzhoy

More information

Decision Trees. Nicholas Ruozzi University of Texas at Dallas. Based on the slides of Vibhav Gogate and David Sontag

Decision Trees. Nicholas Ruozzi University of Texas at Dallas. Based on the slides of Vibhav Gogate and David Sontag Decision Trees Nicholas Ruozzi University of Texas at Dallas Based on the slides of Vibhav Gogate and David Sontag Supervised Learning Input: labelled training data i.e., data plus desired output Assumption:

More information

Bias Correction in Classification Tree Construction ICML 2001

Bias Correction in Classification Tree Construction ICML 2001 Bias Correction in Classification Tree Construction ICML 21 Alin Dobra Johannes Gehrke Department of Computer Science Cornell University December 15, 21 Classification Tree Construction Outlook Temp. Humidity

More information

Data Mining Classification: Basic Concepts, Decision Trees, and Model Evaluation

Data Mining Classification: Basic Concepts, Decision Trees, and Model Evaluation Data Mining Classification: Basic Concepts, Decision Trees, and Model Evaluation Lecture Notes for Chapter 4 Part I Introduction to Data Mining by Tan, Steinbach, Kumar Adapted by Qiang Yang (2010) Tan,Steinbach,

More information

ML techniques. symbolic techniques different types of representation value attribute representation representation of the first order

ML techniques. symbolic techniques different types of representation value attribute representation representation of the first order MACHINE LEARNING Definition 1: Learning is constructing or modifying representations of what is being experienced [Michalski 1986], p. 10 Definition 2: Learning denotes changes in the system That are adaptive

More information

Information Gain, Decision Trees and Boosting ML recitation 9 Feb 2006 by Jure

Information Gain, Decision Trees and Boosting ML recitation 9 Feb 2006 by Jure Information Gain, Decision Trees and Boosting 10-701 ML recitation 9 Feb 2006 by Jure Entropy and Information Grain Entropy & Bits You are watching a set of independent random sample of X X has 4 possible

More information

Building Bayesian Networks. Lecture3: Building BN p.1

Building Bayesian Networks. Lecture3: Building BN p.1 Building Bayesian Networks Lecture3: Building BN p.1 The focus today... Problem solving by Bayesian networks Designing Bayesian networks Qualitative part (structure) Quantitative part (probability assessment)

More information

Jialiang Bao, Joseph Boyd, James Forkey, Shengwen Han, Trevor Hodde, Yumou Wang 10/01/2013

Jialiang Bao, Joseph Boyd, James Forkey, Shengwen Han, Trevor Hodde, Yumou Wang 10/01/2013 Simple Classifiers Jialiang Bao, Joseph Boyd, James Forkey, Shengwen Han, Trevor Hodde, Yumou Wang 1 Overview Pruning 2 Section 3.1: Simplicity First Pruning Always start simple! Accuracy can be misleading.

More information

Chapter 4.5 Association Rules. CSCI 347, Data Mining

Chapter 4.5 Association Rules. CSCI 347, Data Mining Chapter 4.5 Association Rules CSCI 347, Data Mining Mining Association Rules Can be highly computationally complex One method: Determine item sets Build rules from those item sets Vocabulary from before

More information

Introduction to Machine Learning CMU-10701

Introduction to Machine Learning CMU-10701 Introduction to Machine Learning CMU-10701 23. Decision Trees Barnabás Póczos Contents Decision Trees: Definition + Motivation Algorithm for Learning Decision Trees Entropy, Mutual Information, Information

More information

Bayesian Learning Features of Bayesian learning methods:

Bayesian Learning Features of Bayesian learning methods: Bayesian Learning Features of Bayesian learning methods: Each observed training example can incrementally decrease or increase the estimated probability that a hypothesis is correct. This provides a more

More information

Classification: Decision Trees

Classification: Decision Trees Classification: Decision Trees These slides were assembled by Byron Boots, with grateful acknowledgement to Eric Eaton and the many others who made their course materials freely available online. Feel

More information

Information Theory & Decision Trees

Information Theory & Decision Trees Information Theory & Decision Trees Jihoon ang Sogang University Email: yangjh@sogang.ac.kr Decision tree classifiers Decision tree representation for modeling dependencies among input variables using

More information

Algorithms for Classification: The Basic Methods

Algorithms for Classification: The Basic Methods Algorithms for Classification: The Basic Methods Outline Simplicity first: 1R Naïve Bayes 2 Classification Task: Given a set of pre-classified examples, build a model or classifier to classify new cases.

More information

Classification: Rule Induction Information Retrieval and Data Mining. Prof. Matteo Matteucci

Classification: Rule Induction Information Retrieval and Data Mining. Prof. Matteo Matteucci Classification: Rule Induction Information Retrieval and Data Mining Prof. Matteo Matteucci What is Rule Induction? The Weather Dataset 3 Outlook Temp Humidity Windy Play Sunny Hot High False No Sunny

More information

Chapter 3: Decision Tree Learning (part 2)

Chapter 3: Decision Tree Learning (part 2) Chapter 3: Decision Tree Learning (part 2) CS 536: Machine Learning Littman (Wu, TA) Administration Books? Two on reserve in the math library. icml-03: instructional Conference on Machine Learning mailing

More information

COMP 328: Machine Learning

COMP 328: Machine Learning COMP 328: Machine Learning Lecture 2: Naive Bayes Classifiers Nevin L. Zhang Department of Computer Science and Engineering The Hong Kong University of Science and Technology Spring 2010 Nevin L. Zhang

More information

Data Mining vs Statistics

Data Mining vs Statistics Data Mining Data mining emerged in the 1980 s when the amount of data generated and stored became overwhelming. Data mining is strongly influenced by other disciplines such as mathematics, statistics,

More information

Supervised Learning: Regression & Classifiers. Fall 2010

Supervised Learning: Regression & Classifiers. Fall 2010 Supervised Learning: Regression & Classifiers Fall 2010 Problem Statement Given training data of the form: {( xi, yi)...( xm, ym)} X: the space of input features/attributes Y: the space of output values

More information

Classification II: Decision Trees and SVMs

Classification II: Decision Trees and SVMs Classification II: Decision Trees and SVMs Digging into Data: Jordan Boyd-Graber February 25, 2013 Slides adapted from Tom Mitchell, Eric Xing, and Lauren Hannah Digging into Data: Jordan Boyd-Graber ()

More information