Administrative notes. Computational Thinking ct.cs.ubc.ca
|
|
- Ann Ryan
- 5 years ago
- Views:
Transcription
1 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
2 Administrative notes March 3: Data mining reading quiz March 14: Midterm 2 March 17: In the News call #3 March 30: Project deliverables and individual report due
3 Data mining: finding patterns in data Part 1: Building decision tree classifiers from data
4 Learning goals [CT Building Block] Students will be able to build a simple decision tree [CT Building Block] Students will be able to describe what considerations are important in building a decision tree
5 Why data mining? The world is awash with digital data; trillions of gigabytes and growing How many bytes in a gigabyte? Clicker question A B C
6 Why data mining? The world is awash with digital data; trillions of gigabytes and growing A trillion gigabytes is a zettabyte, or bytes
7 Why data mining? More and more, businesses and institutions are using data mining to make decisions, classifications, diagnoses, and recommendations that affect our lives
8 Data mining for classification Recall our loan application example
9 Data mining for classification In the loan strategy example, we focused on fairness of different classifiers, but we didn t focus much on how to build a classifier Today you ll learn how to build decision tree classifiers for simple data mining scenarios
10 A rooted tree in computer science Before we get to decision trees, we ll define what is a tree
11 A rooted tree in computer science A collection of nodes such that one node is the designated root a node can have zero or more children; a node with zero children is a leaf all non-root nodes have a single parent
12 A rooted tree in computer science A collection of nodes such that one node is the designated root a node can have zero or more children; a node with zero children is a leaf all non-root nodes have a single parent edges denote parent-child relationships nodes and/or edges may be labeled by data
13 A rooted tree in computer science Often but not always drawn with root on top
14 Are these rooted trees? Clicker question 1 2 A. 1 but not 2 B. 2 both not 1 C. Both 1 and 2 D. Neither 1 nor 2
15 Is this a rooted tree? Clicker question A. B. C. I m not sure de 2 has two parents, and there s no unique root
16 Decision trees: trees whose node labels are attributes, edge labels are conditions
17 Decision trees: trees whose node labels are attributes, edge labels are conditions
18 Decision trees: trees whose node labels are attributes, edge labels are conditions
19 Decision trees: trees whose node labels are attributes, edge labels are conditions
20 Decision trees: trees whose node labels are attributes, edge labels are conditions
21 Decision trees: trees whose node labels are attributes, edge labels are conditions A decision tree for max profit loan strategy colour credit rating blue orange credit rating > 61 < 61 > 50 < 50 approve deny approve deny (te that some worthy applicants are denied loans, while other unworthy ones get loans)
22 Exercise: Construct the decision tree for the Group Unaware loan strategy
23 Building decision trees from training data Should you get an ice cream? You might start out with the following data Weather Wallet Ice Cream? Great Empty Nasty Empty Great Full Okay Full Nasty Full
24 Building decision trees from training data Should you get an ice cream? You might start out with the following data attributes Weather Wallet Ice Cream? Great Empty Nasty Empty Great Full Okay Full Nasty Full conditions
25 Building decision trees from training data Should you get an ice cream? You might start out with the following data You might build a decision tree that looks like this: attributes Weather Wallet Ice Cream? Great Empty Empty Wallet Full Nasty Empty Great Full Okay Full Nasty Full conditions Nasty Weather Okay Great
26 Shall we play a game? Suppose we want to help a soccer league decide whether on not to cancel games. We have some data. Our goal is a decision tree to help officials make decisions Assume that decisions are the same given the same information Outlook Temperature Humidity Windy Play? sunny hot high false sunny hot high true overcast hot high false rain mild high false rain cool normal false rain cool normal true overcast cool normal true sunny mild high false sunny cool normal false rain mild normal false sunny mild normal true overcast mild high true overcast hot normal false rain mild high true Example adapted from data_mining_course/index.html#materials
27 Create a decision tree Group exercise Create a decision tree that decides whether the game should be played or not The leaf nodes should be whether or not to play The non-leaf nodes should be attributes (e.g., Outlook, Windy) The edges should be conditions (e.g., sunny, hot, normal) Outlook Temperature Humidity Windy Play? sunny hot high false sunny hot high true overcast hot high false rain mild high false rain cool normal false rain cool normal true overcast cool normal true sunny mild high false sunny cool normal false rain mild normal false sunny mild normal true overcast mild high true overcast hot normal false rain mild high true
28 Some example potential starts to the decision tree Outlook? Windy? Overcast Rainy Sunny Temperature? Humidity? Windy? Humidity? true false Humidity?
29 How did you split up your tree and why? Student responses Looked at each condition of every attribute, case by case, until got to the end, working left to right through the columns There are ways that the tree can be made smaller, by finding patterns. E.g., if it s overcast, the answer is always yes.
30 Here s that example again Create a decision tree that decides whether the game should be played or not The leaf nodes should be whether or not to play The non-leaf nodes should be attributes (e.g., Outlook, Windy) The edges should be conditions (e.g., sunny, hot, normal) Outlook Temperature Humidity Windy Play? sunny hot high false sunny hot high true overcast hot high false rain mild high false rain cool normal false rain cool normal true overcast cool normal true sunny mild high false sunny cool normal false rain mild normal false sunny mild normal true overcast mild high true overcast hot normal false rain mild high true
31 Deciding which nodes go where: A decision tree construction algorithm Top-down tree construction At start, all examples are at the root. Partition the examples recursively by choosing one attribute each time. In deciding which attribute to split on, one common method is to try to reduce entropy i.e., each time you split, you should make the resulting groups more homogenous. The more you reduce entropy, the higher the information gain.
32 Let s go back to our example Intuitively, our goal is to get to having as few mixed and answers together in groups as possible. So in the initial case, we have 14 mixed s and s Outlook Temperature Humidity Windy Play? sunny hot high false sunny hot high true overcast hot high false rain mild high false rain cool normal false rain cool normal true overcast cool normal true sunny mild high false sunny cool normal false rain mild normal false sunny mild normal true overcast mild high true overcast hot normal false rain mild high true
33 What happens if we split on Temperature? temperature hot mild cool 4 mixed 4 mixed 6 mixed Overall entropy = = 14
34 What s the entropy if you split on Outlook? Group exercise A. 0 B. 5 C. 10 D. 14 E. ne of the above Outlook Temperature Humidity Windy Play? sunny hot high false sunny hot high true overcast hot high false rain mild high false rain cool normal false rain cool normal true overcast cool normal true sunny mild high false sunny cool normal false rain mild normal false sunny mild normal true overcast mild high true overcast hot normal false rain mild high true
35 What s the entropy if you split on Outlook? Group exercise results Outlook sunny overcast rainy 0 mixed 5 mixed 5 mixed Overall entropy = = 10
36 What if you split on Windy? Windy false true 6 mixed 8 mixed Overall entropy = 8+6=14
37 What if you split on Humidity? Humidity high normal 7 mixed 7 mixed Overall entropy = 7+7=14
38 The best option to split on is Outlook It does the best job of reducing entropy
39 This example suggests why a more complex entropy definition might be better Windy Humidity false true high normal Humidity is better, even though both have entropy 14
40 Great! w we do the same thing again Here s what we have so far: Outlook sunny overcast rainy For each option, we have to decide which attribute to split on next: Temperature, Windy, or Humidity.
41 Great! w we do the same thing again Clicker question What s the best attribute to split on for Outlook = sunny? A. B. C. Temperature Windy Humidity hot mild cool false true high normal
42 We don t need to split for Outlook = overcast The answer was yes each time. So we re done there.
43 What s the best attribute to split on for Outlook = rain? Clicker question A. B. C. Temperature Windy Humidity hot N/A mild cool false true high normal
44 This was, of course, a simple example In this example, the algorithm found the tree with the smallest number of nodes We were given the attributes and conditions A simplistic notion of entropy worked (a more sophisticated notion of entropy is typically used to determine which attribute to split on)
45 This was, of course, a simple example In more complex examples, like the loan application example We may not know which conditions or attributes are best to use The final decision may not be correct in every case (e.g., given two loan applicants with the same colour and credit rating, one may be credit worthy while the other is not) Even if the final decision is always correct, the tree may not be of minimum size
46 Coding up a decision tree classifier Outlook sunny overcast rainy Humidity Windy high normal false true
47 Coding up a decision tree classifier Outlook sunny overcast Humidity high normal Can you see the relationship between the hierarchical tree structure and the hierarchical nesting of if statements?
48 Coding up a decision tree classifier Outlook sunny overcast Humidity high normal Can you extend the code to handle the rainy case?
49 Learning goals [CT Building Block] Students will be able to build a simple decision tree [CT Building Block] Students will be able to describe what considerations are important in building a decision tree
Administrative notes February 27, 2018
Administrative notes February 27, 2018 Welcome back! Reminder: In the News Call #2 due tomorrow Reminder: Midterm #2 is on March 13 Project proposals are all marked. You can resubmit your proposal after
More informationDecision 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 informationThe 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 informationLearning 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 informationLearning 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 informationDecision 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 informationAlgorithms 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 informationDecision 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 informationMachine Learning. Yuh-Jye Lee. March 1, Lab of Data Science and Machine Intelligence Dept. of Applied Math. at NCTU
Machine Learning Yuh-Jye Lee Lab of Data Science and Machine Intelligence Dept. of Applied Math. at NCTU March 1, 2017 1 / 13 Bayes Rule Bayes Rule Assume that {B 1, B 2,..., B k } is a partition of S
More informationClassification: 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( 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 informationArtificial 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 informationDecision 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 informationDecision 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 informationCSE-4412(M) Midterm. There are five major questions, each worth 10 points, for a total of 50 points. Points for each sub-question are as indicated.
22 February 2007 CSE-4412(M) Midterm p. 1 of 12 CSE-4412(M) Midterm Sur / Last Name: Given / First Name: Student ID: Instructor: Parke Godfrey Exam Duration: 75 minutes Term: Winter 2007 Answer the following
More informationDecision 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 informationClassification 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 informationRule 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 informationDecision 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 informationData 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 informationClassification 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 informationDecision 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 informationImagine 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 informationMachine 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 informationhttp://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 informationDecision 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 informationInduction 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 informationBayesian 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 informationDecision 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 informationDecision T ree Tree Algorithm Week 4 1
Decision Tree Algorithm Week 4 1 Team Homework Assignment #5 Read pp. 105 117 of the text book. Do Examples 3.1, 3.2, 3.3 and Exercise 3.4 (a). Prepare for the results of the homework assignment. Due date
More informationDecision 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 informationData Mining. Chapter 1. What s it all about?
Data Mining Chapter 1. What s it all about? 1 DM & ML Ubiquitous computing environment Excessive amount of data (data flooding) Gap between the generation of data and their understanding Looking for structural
More informationUniversität 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 informationMachine 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 informationLearning 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 informationInteligência Artificial (SI 214) Aula 15 Algoritmo 1R e Classificador Bayesiano
Inteligência Artificial (SI 214) Aula 15 Algoritmo 1R e Classificador Bayesiano Prof. Josenildo Silva jcsilva@ifma.edu.br 2015 2012-2015 Josenildo Silva (jcsilva@ifma.edu.br) Este material é derivado dos
More informationSymbolic 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 informationData Mining Prof. Pabitra Mitra Department of Computer Science & Engineering Indian Institute of Technology, Kharagpur
Data Mining Prof. Pabitra Mitra Department of Computer Science & Engineering Indian Institute of Technology, Kharagpur Lecture 21 K - Nearest Neighbor V In this lecture we discuss; how do we evaluate the
More informationIntroduction. 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 informationLecture 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 informationReminders. HW1 out, due 10/19/2017 (Thursday) Group formations for course project due today (1 pt) Join Piazza (
CS 145 Discussion 2 Reminders HW1 out, due 10/19/2017 (Thursday) Group formations for course project due today (1 pt) Join Piazza (email: juwood03@ucla.edu) Overview Linear Regression Z Score Normalization
More informationCS 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 informationArtificial Intelligence. Topic
Artificial Intelligence Topic 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
More informationTutorial 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 informationLeveraging 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 informationDecision 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 informationCS6375: 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 informationDecision-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 informationLecture 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 informationChapter 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 informationDecision 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 informationClassification 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 informationEECS 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 informationClassification: 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 informationSupervised 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 informationBayesian 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 informationDecision 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 informationDECISION 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 informationModern Information Retrieval
Modern Information Retrieval Chapter 8 Text Classification Introduction A Characterization of Text Classification Unsupervised Algorithms Supervised Algorithms Feature Selection or Dimensionality Reduction
More informationThe 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 informationChapter 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 informationTools of AI. Marcin Sydow. Summary. Machine Learning
Machine Learning Outline of this Lecture Motivation for Data Mining and Machine Learning Idea of Machine Learning Decision Table: Cases and Attributes Supervised and Unsupervised Learning Classication
More informationSymbolic 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/cs4062/ kevin.koidl@scss.tcd.ie, maldonaa@tcd.ie 2016-2017 2
More informationData 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 informationMachine 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 informationDan 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 informationUVA 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 informationAdministration. 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 informationAnswers Machine Learning Exercises 2
nswers Machine Learning Exercises 2 Tim van Erven October 7, 2007 Exercises. Consider the List-Then-Eliminate algorithm for the EnjoySport example with hypothesis space H = {?,?,?,?,?,?, Sunny,?,?,?,?,?,
More informationDecision 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 informationDecision 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 informationDecision 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 informationEinführung in Web- und Data-Science
Einführung in Web- und Data-Science Prof. Dr. Ralf Möller Universität zu Lübeck Institut für Informationssysteme Tanya Braun (Übungen) Inductive Learning Chapter 18/19 Chapters 3 and 4 Material adopted
More informationDecision 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 informationData Mining and Machine Learning
Data Mining and Machine Learning Concept Learning and Version Spaces Introduction Concept Learning Generality Relations Refinement Operators Structured Hypothesis Spaces Simple algorithms Find-S Find-G
More informationthe 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 informationIntroduction 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 informationIntroduction 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 informationQuestion 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 informationDecision Tree. Decision Tree Learning. c4.5. Example
Decision ree Decision ree Learning s of systems that learn decision trees: c4., CLS, IDR, ASSISA, ID, CAR, ID. Suitable problems: Instances are described by attribute-value couples he target function has
More informationCLASSIFICATION NAIVE BAYES. NIKOLA MILIKIĆ UROŠ KRČADINAC
CLASSIFICATION NAIVE BAYES NIKOLA MILIKIĆ nikola.milikic@fon.bg.ac.rs UROŠ KRČADINAC uros@krcadinac.com WHAT IS CLASSIFICATION? A supervised learning task of determining the class of an instance; it is
More informationMidterm. You may use a calculator, but not any device that can access the Internet or store large amounts of data.
INST 737 April 1, 2013 Midterm Name: }{{} by writing my name I swear by the honor code Read all of the following information before starting the exam: For free response questions, show all work, clearly
More information} It is non-zero, and maximized given a uniform distribution } Thus, for any distribution possible, we have:
Review: Entropy and Information H(P) = X i p i log p i Class #04: Mutual Information & Decision rees Machine Learning (CS 419/519): M. Allen, 1 Sept. 18 } Entropy is the information gained on average when
More informationNaïve Bayes Lecture 6: Self-Study -----
Naïve Bayes Lecture 6: Self-Study ----- Marina Santini Acknowledgements Slides borrowed and adapted from: Data Mining by I. H. Witten, E. Frank and M. A. Hall 1 Lecture 6: Required Reading Daumé III (015:
More informationJialiang 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 informationMachine 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 informationDecision 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 informationDecision 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 informationData 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 informationOutline. 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 informationMachine Learning 2010
Machine Learning 2010 Decision Trees Email: mrichter@ucalgary.ca -- 1 - Part 1 General -- 2 - Representation with Decision Trees (1) Examples are attribute-value vectors Representation of concepts by labeled
More informationInductive Learning. Chapter 18. Material adopted from Yun Peng, Chuck Dyer, Gregory Piatetsky-Shapiro & Gary Parker
Inductive Learning Chapter 18 Material adopted from Yun Peng, Chuck Dyer, Gregory Piatetsky-Shapiro & Gary Parker Chapters 3 and 4 Inductive Learning Framework Induce a conclusion from the examples Raw
More informationName (NetID): (1 Point)
CS446: Machine Learning Fall 2016 October 25 th, 2016 This is a closed book exam. Everything you need in order to solve the problems is supplied in the body of this exam. This exam booklet contains four
More informationDecision Trees. None Some Full > No Yes. No Yes. No Yes. No Yes. No Yes. No Yes. No Yes. Patrons? WaitEstimate? Hungry? Alternate?
Decision rees Decision trees is one of the simplest methods for supervised learning. It can be applied to both regression & classification. Example: A decision tree for deciding whether to wait for a place
More informationWeather Prediction Using Historical Data
Weather Prediction Using Historical Data COMP 381 Project Report Michael Smith 1. Problem Statement Weather prediction is a useful tool for informing populations of expected weather conditions. Weather
More informationML 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 informationCS 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 informationClassification 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 informationBayesian Learning. Bayesian Learning Criteria
Bayesian Learning In Bayesian learning, we are interested in the probability of a hypothesis h given the dataset D. By Bayes theorem: P (h D) = P (D h)p (h) P (D) Other useful formulas to remember are:
More informationEnsemble Methods. Charles Sutton Data Mining and Exploration Spring Friday, 27 January 12
Ensemble Methods Charles Sutton Data Mining and Exploration Spring 2012 Bias and Variance Consider a regression problem Y = f(x)+ N(0, 2 ) With an estimate regression function ˆf, e.g., ˆf(x) =w > x Suppose
More information