Data mining techniques: decision trees
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1 Data mining techniques: decision trees 1/39 Agenda Rule systems Building rule systems vs rule systems Quick reference 2/39 1
2 Agenda Rule systems Building rule systems vs rule systems Quick reference 3/39 Set of conditions organized hierarchically in such a way that the final decision can be determined following the conditions that are fulfilled from the root of the tree to one of its leaves. They are easily understandable. They build a model (made up by rules) easy to understand for the user. They only work over a single table, and over a single attribute at a time. They are one of the most used data mining techniques. 4/39 2
3 The weather problem: 5/39 Decision tree that solves the problem: 6/39 3
4 The decision trees are based on the strategy "divide and conquer". There are two possible types of divisions or partitions: Nominal partitions: a nominal attribute may lead to a split with as many branches as values there are for the attribute. Numerical partitions: typically, they allow partitions like "X>" and "X <a". Partitions relating two different attributes are not permitted. What distinguishes the different algorithms from each others are the partitions they allow, and what criteria they use to select the partitions. 7/39 Expressive power: they can only make partitions parallel to the axis: 2D example 8/39 4
5 Agenda Rule systems Building rule systems vs rule systems Quick reference 9/39 The most common approach is to try to minimize the function like n is the number of partitions p j is the probability of falling into the node j p c j is the proportion of elements of the class c falling into the node j. f is a function of impurity, thus the formula calculates the average "impurity" of the nodes. 10/39 5
6 Some functions: Error GINI (CART) Entropy (C4.5) DKM The more entropy there is, the more information is needed to solve the problem. Note: log are taken in base 2. 11/39 The wheather problem: Which is the best decision? Outlook: 12/39 6
7 But how much increase in "purity" (information) would that choice produce? What was the "purity" at the root of the tree? Thus, we are gaining: 13/39 Thus, the info gain for every possible branch is: 14/39 7
8 Once the first decision attribute has been chosen, we repeat the operation with the other nodes: 15/39 Final result: With real examples, a certain level of "impurity" in each leaf node is usually tolerated. Trying to learn the training data perfectly, will likely lead to overfitting. 16/39 8
9 But 17/39 This attribute gives us a perfect (and useless) classification!!! Its info gain is bits, i.e., all the information needed to solve the problem : 18/39 9
10 The solution to this problem is to somehow penalize the attributes that lead to a very high number of branches. One option is to take into account the number and size of the children nodes, regardless of what classes they contain. For our ID attribute: This is bits. We shall call this value information value of the attribute. 19/39 Instead of using "gain" to determine which attribute to use for the next branch, we shall use "gain ratio" which we shall define as the division between gain and the information value of the attribute. For our ID attribute: gain ratio (ID)= 0.940/3.807= For the initial branch: 20/39 10
11 The calculation method presented here is the one used in WEKA for the algorithm C4.5. It is one of the best algorithms, both from a theoretical point of view and in practice. A commercial variant (C5) implemented in Clementine has a slightly better performance. 21/39 Often, too much adjusting to the training data is counterproductive (overtraining). One solution is to remove some branches of the tree ( "pruning"). Two pruning strategies: Pre-pruning: the process is done during the construction of the tree. There is some criteria to stop expanding the nodes (allowing a certain level of "impurity" in each node). Post-pruning: the process is done after the construction of the tree. Branches are removed from the bottom up to a certain limit. It uses similar criteria to pre-pruning. 22/39 11
12 The key to knowing when and how much to prune is to check the behavior of the tree with the test data. With the training data the behavior of the tree will only improve. Pruning is the key to dealing with noisy data. 23/39 Agenda Rule systems Building rule systems vs rule systems Quick reference 24/39 12
13 Rule systems In rule systems the rules are not organized hierarchically, nor must they provide a classification for all the examples. There may be instances that are not covered by the rule set. Instead of building the rules by "divide and conquer" they use an approach based on coverage. They try to create rules that "cover" all instances of the training data set. 25/39 Rule systems Example: 26/39 13
14 Rule systems Example: 27/39 Agenda Rule systems Building rule systems vs rule systems Quick reference 28/39 14
15 Building rule systems Another example: we have to determine the recommended type of contact lens. Some of the data: 29/39 Building rule systems All the data: Column 2, age of the patient: (1) young, (2) pre-presbyopic, (3) presbyopic. Column 3, spectacle prescription: (1) myope, (2) hypermetrope. Column 4, astigmatic: (1) no, (2) yes Column 5, tear production rate: (1) reduced, (2) normal. Column 6, contact lenses: (1) hard, (2) soft, (3) no contact lenses. 30/39 15
16 Building rule systems We start by looking for conditions that will enable us to determine whether the lenses should be hard: To do this, we calculate the number of instances for which, given the value of an attribute, we can adequately predict that the contact lenses should be hard. We then select the attribute that covers a higher percentage of instances that meet the desired criteria (hard contact lenses recommended). 31/39 Building rule systems Selection process: 32/39 16
17 Building rule systems We now try to properly classify the other 8 instances covered by the rule: 33/39 Building rule systems And we try to properly classify the other 2 instances covered by the rule: We now have a rule that classifies perfectly the three instances it covers. Now we start the process again, and so on until we have rules that cover all (or most) instances. 34/39 17
18 Agenda Rule systems Building rule systems vs rule systems Quick reference 35/39 vs rule systems The decision trees are often more efficient because each partition generates two rules. Pruning of trees is often simpler than the pruning of rules systems (although the same criteria are used in both cases). Rule systems permit non-exhaustive coverage, which is interesting if an attribute has many values, but not all are relevant. Rule systems are less voracious, so they tend to fall less in local minima. 36/39 18
19 Agenda Rule systems Building rule systems vs rule systems Quick reference 37/39 Quick reference They are used mainly for classification (supervised learning), although it is possible to use them for regression, clustering and estimation of probabilities. They are relatively efficient and capable of working with large volumes of data. They work with numeric and nominal attributes. They only work over a single table and a single attribute at a time. They do not allow relations between two attributes (for efficiency). They are tolerant to noise, not significant attributes and missing values. They build a model (rules) that is easy to understand and interpret by the end user. 38/39 19
20 Quick reference Visual comparison of their expressive power: Rule systems 39/39 20
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Reference Books. Data Mining. Supervised vs. Unsupervised Learning. Classification: Definition. Classification k-nearest neighbors
Classification k-nearest neighbors Data Mining Dr. Engin YILDIZTEPE Reference Books Han, J., Kamber, M., Pei, J., (2011). Data Mining: Concepts and Techniques. Third edition. San Francisco: Morgan Kaufmann
A Decision Tree for Weather Prediction
BULETINUL UniversităŃii Petrol Gaze din Ploieşti Vol. LXI No. 1/2009 77-82 Seria Matematică - Informatică - Fizică A Decision Tree for Weather Prediction Elia Georgiana Petre Universitatea Petrol-Gaze
from Larson Text By Susan Miertschin
Decision Tree Data Mining Example from Larson Text By Susan Miertschin 1 Problem The Maximum Miniatures Marketing Department wants to do a targeted mailing gpromoting the Mythic World line of figurines.
D A T A M I N I N G C L A S S I F I C A T I O N
D A T A M I N I N G C L A S S I F I C A T I O N FABRICIO VOZNIKA LEO NARDO VIA NA INTRODUCTION Nowadays there is huge amount of data being collected and stored in databases everywhere across the globe.
Mauro Sousa Marta Mattoso Nelson Ebecken. and these techniques often repeatedly scan the. entire set. A solution that has been used for a
Data Mining on Parallel Database Systems Mauro Sousa Marta Mattoso Nelson Ebecken COPPEèUFRJ - Federal University of Rio de Janeiro P.O. Box 68511, Rio de Janeiro, RJ, Brazil, 21945-970 Fax: +55 21 2906626
Optimization of C4.5 Decision Tree Algorithm for Data Mining Application
Optimization of C4.5 Decision Tree Algorithm for Data Mining Application Gaurav L. Agrawal 1, Prof. Hitesh Gupta 2 1 PG Student, Department of CSE, PCST, Bhopal, India 2 Head of Department CSE, PCST, Bhopal,
Decision Tree Induction in High Dimensional, Hierarchically Distributed Databases
Decision Tree Induction in High Dimensional, Hierarchically Distributed Databases Amir Bar-Or, Assaf Schuster, Ran Wolff Faculty of Computer Science Technion, Israel {abaror, assaf, ranw}@cs.technion.ac.il
Index Contents Page No. Introduction . Data Mining & Knowledge Discovery
Index Contents Page No. 1. Introduction 1 1.1 Related Research 2 1.2 Objective of Research Work 3 1.3 Why Data Mining is Important 3 1.4 Research Methodology 4 1.5 Research Hypothesis 4 1.6 Scope 5 2.