Introduction to Machine Learning

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1 Introduction to Machine Learning D. De Cao R. Basili Corso di Web Mining e Retrieval a.a April 6, 2009

2 Outline Outline Introduction to Machine Learning

3 Outline Outline Introduction to Machine Learning Decision Tree

4 Outline Outline Introduction to Machine Learning Decision Tree Naive Bayes

5 Outline Outline Introduction to Machine Learning Decision Tree Naive Bayes K-nearest neighbor

6 Introduction to Machine Learning Like human learning from past experiences.

7 Introduction to Machine Learning Like human learning from past experiences. A computer system learns from data, which represent some past experiences of an application domain.

8 Introduction to Machine Learning Like human learning from past experiences. A computer system learns from data, which represent some past experiences of an application domain. Our focus: learn a target function that can be used to predict the values of a discrete class attribute.

9 Introduction to Machine Learning Like human learning from past experiences. A computer system learns from data, which represent some past experiences of an application domain. Our focus: learn a target function that can be used to predict the values of a discrete class attribute. The task is commonly called: Supervised learning, classification.

10 Introduction to Machine Learning Example You need to write a program that: given a Level Hierarchy of a company

11 Introduction to Machine Learning Example You need to write a program that: given a Level Hierarchy of a company given an employe described trough some attributes (the number of attributes can be very high)

12 Introduction to Machine Learning Example You need to write a program that: given a Level Hierarchy of a company given an employe described trough some attributes (the number of attributes can be very high) assign to the employe the correct level into the hierarchy.

13 Introduction to Machine Learning Example You need to write a program that: given a Level Hierarchy of a company given an employe described trough some attributes (the number of attributes can be very high) assign to the employe the correct level into the hierarchy. How many if are necessary to select the correct level?

14 Introduction to Machine Learning Example You need to write a program that: given a Level Hierarchy of a company given an employe described trough some attributes (the number of attributes can be very high) assign to the employe the correct level into the hierarchy. How many if are necessary to select the correct level? How many time is necessary to study the relations between the hierarchy and attributes?

15 Introduction to Machine Learning Example You need to write a program that: given a Level Hierarchy of a company given an employe described trough some attributes (the number of attributes can be very high) assign to the employe the correct level into the hierarchy. How many if are necessary to select the correct level? How many time is necessary to study the relations between the hierarchy and attributes? Solution Learn the function to link each employe to the correct level.

16 Supervised Learning: Data and Goal Data: a set of data records (also called examples, instances or cases) described by: k attributes: A 1,A 2,...,A k.

17 Supervised Learning: Data and Goal Data: a set of data records (also called examples, instances or cases) described by: k attributes: A 1,A 2,...,A k. a class: Each example is labelled with a pre-defined class.

18 Supervised Learning: Data and Goal Data: a set of data records (also called examples, instances or cases) described by: k attributes: A 1,A 2,...,A k. a class: Each example is labelled with a pre-defined class. In previous example data can be obtained from existing DataBase.

19 Supervised Learning: Data and Goal Data: a set of data records (also called examples, instances or cases) described by: k attributes: A 1,A 2,...,A k. a class: Each example is labelled with a pre-defined class. In previous example data can be obtained from existing DataBase. Goal: to learn a classification model from the data that can be used to predict the classes of new (future, or test) cases/instances.

20 Supervised vs. Unsupervised Learning Supervised Learning Needs supervision: The data (observations, measurements, etc.) are labeled with pre-defined classes. It is like that a?teacher? gives the classes.

21 Supervised vs. Unsupervised Learning Supervised Learning Needs supervision: The data (observations, measurements, etc.) are labeled with pre-defined classes. It is like that a?teacher? gives the classes. New data (Test) are classified into these classes too.

22 Supervised vs. Unsupervised Learning Supervised Learning Needs supervision: The data (observations, measurements, etc.) are labeled with pre-defined classes. It is like that a?teacher? gives the classes. New data (Test) are classified into these classes too. Unsupervised Learning Class labels of the data are unknown Given a set of data, the task is to establish the existence of classes or clusters in the data.

23 Supervised Learning process: two steps Learning (Training) Learn a model using the training data

24 Supervised Learning process: two steps Learning (Training) Learn a model using the training data Testing Test the model using unseen test data to assess the model accuracy

25 Supervised Learning process: two steps Learning (Training) Learn a model using the training data Testing Test the model using unseen test data to assess the model accuracy

26 Learning Algorithms Boolean Functions (Decision Trees)

27 Learning Algorithms Boolean Functions (Decision Trees) Probabilistic Functions (Bayesian Classifier)

28 Learning Algorithms Boolean Functions (Decision Trees) Probabilistic Functions (Bayesian Classifier) Functions to partitioning Vector Space

29 Learning Algorithms Boolean Functions (Decision Trees) Probabilistic Functions (Bayesian Classifier) Functions to partitioning Vector Space Non-Linear: KNN, Neural Networks,...

30 Learning Algorithms Boolean Functions (Decision Trees) Probabilistic Functions (Bayesian Classifier) Functions to partitioning Vector Space Non-Linear: KNN, Neural Networks,... Linear: Support Vector Machines, Perceptron,...

31 Decision Tree: Domain Example The class to learn is: approve a loan

32 Decision Tree Decision Tree example for the loan problem

33 Is the decision tree unique? No. Here is a simpler tree.

34 Is the decision tree unique? No. Here is a simpler tree. We want smaller tree and accurate tree. Easy to understand and perform better.

35 Is the decision tree unique? No. Here is a simpler tree. We want smaller tree and accurate tree. Easy to understand and perform better.

36 Is the decision tree unique? No. Here is a simpler tree. We want smaller tree and accurate tree. Easy to understand and perform better. Finding the best tree is NP-hard.

37 Is the decision tree unique? No. Here is a simpler tree. We want smaller tree and accurate tree. Easy to understand and perform better. Finding the best tree is NP-hard. All current tree building algorithms are heuristic algorithms

38 Is the decision tree unique? No. Here is a simpler tree. We want smaller tree and accurate tree. Easy to understand and perform better. Finding the best tree is NP-hard. All current tree building algorithms are heuristic algorithms A decision tree can be converted to a set of rules.

39 From a decision tree to a set of rules

40 From a decision tree to a set of rules Each path from the root to a leaf is a rule

41 From a decision tree to a set of rules Each path from the root to a leaf is a rule Rules Own_house = true Class = yes Own_house = false, Has_job = true Class = yes Own_house = false, Has_job = false Class = no

42 Algorithm for decision tree learning Basic algorithm (a greedy divide-and-conquer algorithm) Assume attributes are categorical now (continuous attributes can be handled too)

43 Algorithm for decision tree learning Basic algorithm (a greedy divide-and-conquer algorithm) Assume attributes are categorical now (continuous attributes can be handled too) Tree is constructed in a top-down recursive manner

44 Algorithm for decision tree learning Basic algorithm (a greedy divide-and-conquer algorithm) Assume attributes are categorical now (continuous attributes can be handled too) Tree is constructed in a top-down recursive manner At start, all the training examples are at the root

45 Algorithm for decision tree learning Basic algorithm (a greedy divide-and-conquer algorithm) Assume attributes are categorical now (continuous attributes can be handled too) Tree is constructed in a top-down recursive manner At start, all the training examples are at the root Examples are partitioned recursively based on selected attributes

46 Algorithm for decision tree learning Basic algorithm (a greedy divide-and-conquer algorithm) Assume attributes are categorical now (continuous attributes can be handled too) Tree is constructed in a top-down recursive manner At start, all the training examples are at the root Examples are partitioned recursively based on selected attributes Attributes are selected on the basis of an impurity function (e.g., information gain)

47 Algorithm for decision tree learning Basic algorithm (a greedy divide-and-conquer algorithm) Assume attributes are categorical now (continuous attributes can be handled too) Tree is constructed in a top-down recursive manner At start, all the training examples are at the root Examples are partitioned recursively based on selected attributes Attributes are selected on the basis of an impurity function (e.g., information gain) Conditions for stopping partitioning All examples for a given node belong to the same class

48 Algorithm for decision tree learning Basic algorithm (a greedy divide-and-conquer algorithm) Assume attributes are categorical now (continuous attributes can be handled too) Tree is constructed in a top-down recursive manner At start, all the training examples are at the root Examples are partitioned recursively based on selected attributes Attributes are selected on the basis of an impurity function (e.g., information gain) Conditions for stopping partitioning All examples for a given node belong to the same class There are no remaining attributes for further partitioning? majority class is the leaf

49 Algorithm for decision tree learning Basic algorithm (a greedy divide-and-conquer algorithm) Assume attributes are categorical now (continuous attributes can be handled too) Tree is constructed in a top-down recursive manner At start, all the training examples are at the root Examples are partitioned recursively based on selected attributes Attributes are selected on the basis of an impurity function (e.g., information gain) Conditions for stopping partitioning All examples for a given node belong to the same class There are no remaining attributes for further partitioning? majority class is the leaf There are no examples left

50 Choose an attribute to partition data How chose the best attribute set?

51 Choose an attribute to partition data How chose the best attribute set? The objective is to reduce the impurity or uncertainty in data as much as possible

52 Choose an attribute to partition data How chose the best attribute set? The objective is to reduce the impurity or uncertainty in data as much as possible A subset of data is pure if all instances belong to the same class.

53 Choose an attribute to partition data How chose the best attribute set? The objective is to reduce the impurity or uncertainty in data as much as possible A subset of data is pure if all instances belong to the same class. The heuristic is to choose the attribute with the maximum Information Gain or Gain Ratio based on information theory.

54 Choose an attribute to partition data How chose the best attribute set? The objective is to reduce the impurity or uncertainty in data as much as possible A subset of data is pure if all instances belong to the same class. The heuristic is to choose the attribute with the maximum Information Gain or Gain Ratio based on information theory.

55 Information Gain Entropy of D Given a set of examples D is possible to compute the original entropy of the dataset such as: C H[D] = P(c j )log 2 P(c j ) where C is the set of desired class. j=1

56 Information Gain Entropy of D Given a set of examples D is possible to compute the original entropy of the dataset such as: C H[D] = P(c j )log 2 P(c j ) where C is the set of desired class. j=1 Entropy of an attribute A i If we make attribute A i, with v values, the root of the current tree, this will partition D into v subsets D 1,D 2,...,D v. The expected entropy if A i is used as the current root: H Ai [D] = v j=1 D j D H[D j]

57 Information Gain Information Gain Information gained by selecting attribute A i to branch or to partition the data is given by the difference of prior entropy and the entropy of selected branch gain(d,a i ) = H[D] H Ai [D]

58 Information Gain Information Gain Information gained by selecting attribute A i to branch or to partition the data is given by the difference of prior entropy and the entropy of selected branch gain(d,a i ) = H[D] H Ai [D] We choose the attribute with the highest gain to branch/split the current tree.

59 Example

60 Example H[D] = 6 15 log log = H OH [D] = 6 15 H[D 1] 9 15 H[D 2] = =

61 Example gain(d,age) = = gain(d,own_house) = = gain(d,has_job) = = gain(d,credit) = = H[D] = 6 15 log log = H OH [D] = 6 15 H[D 1] 9 15 H[D 2] = =

62 Example gain(d,age) = = gain(d,own_house) = = gain(d,has_job) = = gain(d,credit) = = H[D] = 6 15 log log = H OH [D] = 6 15 H[D 1] 9 15 H[D 2] = =

63 Algorithm for decision tree learning

P(A, B) = P(A B) = P(A) + P(B) - P(A B)

P(A, B) = P(A B) = P(A) + P(B) - P(A B) AND Probability P(A, B) = P(A B) = P(A) + P(B) - P(A B) P(A B) = P(A) + P(B) - P(A B) Area = Probability of Event AND Probability P(A, B) = P(A B) = P(A) + P(B) - P(A B) If, and only if, A and B are independent,

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