Decision tree algorithm short Weka tutorial
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1 Decision tree algorithm short Weka tutorial Croce Danilo, Roberto Basili Machine leanring for Web Mining a.a
2 Machine Learning: brief summary 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.
3 Machine Learning: brief summary 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?
4 Machine Learning: brief summary 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?
5 Machine Learning: brief summary 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.
6 Supervised Learning process: two steps Learning (Training) Learn a model using the training data
7 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
8 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
9 Learning Algorithms Probabilistic Functions (Bayesian Classifier) Functions to partitioning Vector Space Non-Linear: KNN, Neural Networks,... Linear: Support Vector Machines, Perceptron,...
10 Learning Algorithms Probabilistic Functions (Bayesian Classifier) Functions to partitioning Vector Space Non-Linear: KNN, Neural Networks,... Linear: Support Vector Machines, Perceptron,... Boolean Functions (Decision Trees)
11 Decision Tree: Domain Example The class to learn is: approve a loan
12 Decision Tree Decision Tree example for the loan problem
13 Is the decision tree unique? No. Here is a simpler tree.
14 Is the decision tree unique? No. Here is a simpler tree. We want smaller tree and accurate tree. Easy to understand and perform better.
15 Is the decision tree unique? No. Here is a simpler tree. We want smaller tree and accurate tree. Easy to understand and perform better.
16 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.
17 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
18 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.
19 From a decision tree to a set of rules
20 From a decision tree to a set of rules Each path from the root to a leaf is a rule
21 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
22 Choose an attribute to partition data How chose the best attribute set?
23 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
24 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.
25 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.
26 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.
27 Information Gain Entropy of D Entropy is a measure of the uncertainty associated with a random variable. Given a set of examples D is possible to compute the original entropy of the dataset such as: C H[D] = j=1 where C is the set of desired class. P(c j )log 2 P(c j )
28 Entropy As the data become purer and purer, the entropy value becomes smaller and smaller.
29 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]
30 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]
31 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.
32 Decision Tree: Domain Example Back to the example The class to learn is: approve a loan
33 Example 9 examples belong to "YES" category and 6 to "NO". Exploiting prior knowledge we have: C H[D] = P(c j )log 2 P(c j ) j=1 H[D] = 6 15 log log = 0.971
34 Example 9 examples belong to "YES" category and 6 to "NO". Exploiting prior knowledge we have: C H[D] = P(c j )log 2 P(c j ) j=1 H[D] = 6 15 log log = while partitioning through the Age feature: where H Age [D] = 5 15 H[D 1] 5 15 H[D 2] 5 15 H[D 3] = H[D 1 ] = log 3 2( ) log 2 2( ) = H[D 2 ] = log 2 2( ) log 3 2( ) = H[D 3 ] = log 1 2( ) log 4 2( ) = 0.722
35 Example
36 Example H[D] = 6 15 log log = H OH [D] = 6 15 H[D 1] 9 15 H[D 2] = =
37 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] = =
38 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] = =
39 Algorithm for decision tree learning Basic algorithm (a greedy divide-and-conquer algorithm) Assume attributes are categorical now
40 Algorithm for decision tree learning Basic algorithm (a greedy divide-and-conquer algorithm) Assume attributes are categorical now Tree is constructed in a top-down recursive manner
41 Algorithm for decision tree learning Basic algorithm (a greedy divide-and-conquer algorithm) Assume attributes are categorical now Tree is constructed in a top-down recursive manner At start, all the training examples are at the root
42 Algorithm for decision tree learning Basic algorithm (a greedy divide-and-conquer algorithm) Assume attributes are categorical now 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
43 Algorithm for decision tree learning Basic algorithm (a greedy divide-and-conquer algorithm) Assume attributes are categorical now 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)
44 Algorithm for decision tree learning Basic algorithm (a greedy divide-and-conquer algorithm) Assume attributes are categorical now 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
45 Algorithm for decision tree learning Basic algorithm (a greedy divide-and-conquer algorithm) Assume attributes are categorical now 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
46 Algorithm for decision tree learning Basic algorithm (a greedy divide-and-conquer algorithm) Assume attributes are categorical now 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
47 Algorithm for decision tree learning
48 What is?
49 What is? Collection of ML algorithms - open-source Java package Site: Documentation: documentation.html
50 What is? Collection of ML algorithms - open-source Java package Site: Documentation: documentation.html Schemes for classification include: decision trees, rule learners, naive Bayes, decision tables, locally weighted regression, SVMs, instance-based learners, logistic regression, voted perceptrons, multi-layer perceptron
51 What is? Collection of ML algorithms - open-source Java package Site: Documentation: documentation.html Schemes for classification include: decision trees, rule learners, naive Bayes, decision tables, locally weighted regression, SVMs, instance-based learners, logistic regression, voted perceptrons, multi-layer perceptron For classification, Weka allows train/test split or Cross-fold validation Schemes for clustering: EM and Cobweb
52 ARFF File Format Require
53 ARFF File Format Require @RELATION declaration associates a name with the <relation-name>
54 ARFF File Format Require @RELATION declaration associates a name with the declaration specifies the name and type of an <attribute-name> <datatype> Datatype can be numeric, nominal, string or date
55 ARFF File Format Require @RELATION declaration associates a name with the declaration specifies the name and type of an <attribute-name> <datatype> Datatype can be numeric, nominal, string or sepallength petalwidth class {Setosa,Versicolor,Virginica}
56 ARFF File Format Require @RELATION declaration associates a name with the declaration specifies the name and type of an <attribute-name> <datatype> Datatype can be numeric, nominal, string or sepallength petalwidth class declaration is a single line denoting the start of the data segment Missing values are represented by?
57 ARFF File Format Require @RELATION declaration associates a name with the declaration specifies the name and type of an <attribute-name> <datatype> Datatype can be numeric, nominal, string or sepallength petalwidth class declaration is a single line denoting the start of the data segment Missing values are represented 1.4, 0.2, Setosa 1.4,?, Versicolor
58 ARFF Sparse File Format Similar to AARF files except that data value 0 are not represented
59 ARFF Sparse File Format Similar to AARF files except that data value 0 are not represented Non-zero attributes are specified by attribute number and value
60 ARFF Sparse File Format Similar to AARF files except that data value 0 are not represented Non-zero attributes are specified by attribute number and value 0, X, 0, Y, class A" 0, 0, W, 0, class B"
61 ARFF Sparse File Format Similar to AARF files except that data value 0 are not represented Non-zero attributes are specified by attribute number and value 0, X, 0, Y, class A" 0, 0, W, 0, class B" {1 X, 3 Y, 4 class A"} {2 W, 4 class B"}
62 ARFF Sparse File Format Similar to AARF files except that data value 0 are not represented Non-zero attributes are specified by attribute number and value 0, X, 0, Y, class A" 0, 0, W, 0, class B" {1 X, 3 Y, 4 class A"} {2 W, 4 class B"} Note that the omitted values in a sparse instance are 0, they are not missing values! If a value is unknown, you must explicitly represent it with a question mark (?)
63 Running Learning Schemes java -Xmx512m -cp weka.jar <learner class> [options]
64 Running Learning Schemes java -Xmx512m -cp weka.jar <learner class> [options] Example learner classes: Decision Tree: weka.classifiers.trees.j48 Naive Bayes: weka.classifiers.bayes.naivebayes k-nn: weka.classifiers.lazy.ibk
65 Running Learning Schemes java -Xmx512m -cp weka.jar <learner class> [options] Example learner classes: Decision Tree: weka.classifiers.trees.j48 Naive Bayes: weka.classifiers.bayes.naivebayes k-nn: weka.classifiers.lazy.ibk Important generic options: -t <training file> Specify training file -T <test files> Specify Test file. If none testing is performed on training data -x <number of folds> Number of folds for cross-validation -l <input file> Use saved model -d <output file> Output model to file -split-percentage <train size> Size of training set -c <class index> Index of attribute to use as class (NB: the index start from 1) -p <attribute index> Only output the predictions and one attribute (0 for none) for all test instances.
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