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Machine learning Lecture 3
 

Machine learning Lecture 3

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Machine learning lecture series by Ravi Gupta, AU-KBC in MIT

Machine learning lecture series by Ravi Gupta, AU-KBC in MIT

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    Machine learning Lecture 3 Machine learning Lecture 3 Presentation Transcript

    • Lecture No. 3 Ravi Gupta AU-KBC Research Centre, MIT Campus, Anna University Date: 12.3.2008
    • Today’s Agenda • Recap of ID3 Algorithm • Machine Learning Bias • Occam’s razor principle • Handling ID3 problems
    • Decision Trees • Decision tree learning is a method for approximating discrete value target functions, in which the learned function is represented by a decision tree. • Decision trees can also be represented by if-then-else rule. • Decision tree learning is one of the most widely used approach for inductive inference .
    • Decision Trees Edges: Attribute value Intermediate Nodes: Attributes Attribute: A1 Attribute Attribute value Attribute value value Attribute: A2 Output Attribute: A3 value Attribute Attribute Attribute Attribute value value value value Output Output Output Output value value value value Leave node: Output value
    • Decision Trees Representation conjunction disjunction
    • Decision Trees as If-then-else rule conjunction disjunction •If (Outlook = Sunny AND humidity = Normal) then PlayTennis = Yes •If (Outlook = Overcast) then PlayTennis = Yes •If (Outlook = Rain AND Wind = Weak) then PlayTennis = Yes
    • Problems Suitable for Decision Trees • Instances are represented by attribute-value pairs • The target function has discrete output values • Disjunctive descriptions may be required • The training data may contain errors • The training data may contain missing attribute values
    • Building Decision Tree Attribute: A1 Attribute value Attribute value Attribute value Output value Attribute: A2 Attribute: A3 Attribute value Attribute value Attribute value Attribute value Output value Output value Output value Output value
    • Building Decision Tree Outlook Temperature Which attribute to select ????? Humidity Wind Root node
    • Entropy Given a collection S, containing positive and negative examples of some target concept, the entropy of S relative to this boolean classification (yes/no) is where is the proportion of positive examples in S and pӨ, is the proportion of negative examples in S. In all calculations involving entropy we define 0 log 0 to be 0.
    • Information Gain Measure Information gain, is simply the expected reduction in entropy caused by partitioning the examples according to this attribute. More precisely, the information gain, Gain(S, A) of an attribute A, relative to a collection of examples S, is defined as where Values(A) is the set of all possible values for attribute A, and Sv, is the subset of S for which attribute A has value v, i.e.,
    • Information Gain Measure Entropy of S after Entropy of S partition Gain(S, A) is the expected reduction in entropy caused by knowing the value of attribute A. Gain(S, A) is the information provided about the target &action value, given the value of some other attribute A. The value of Gain(S, A) is the number of bits saved when encoding the target value of an arbitrary member of S, by knowing the value of attribute A.
    • Example There are 14 examples. 9 positive and 5 negative examples [9+, 5-]. The entropy of S relative to this boolean (yes/no) classification is
    • Gain (S, Attribute = Wind)
    • Final Decision Tree
    • Some Insights into Capabilities and Limitations of ID3 Algorithm • ID3’s algorithm searches complete hypothesis space. [Advantage] • ID3 maintain only a single current hypothesis as it searches through the space of decision trees. By determining only as single hypothesis, ID3 loses the capabilities that follows explicitly representing all consistent hypothesis. [Disadvantage] • ID3 in its pure form performs no backtracking in its search. Once it selects an attribute to test at a particular level in the tree, it never backtracks to reconsider this choice. Therefore, it is susceptible to the usual risks of hill-climbing search without backtracking: converging to locally optimal solutions that are not globally optimal. [Disadvantage]
    • Some Insights into Capabilities and Limitations of ID3 Algorithm • ID3 uses all training examples at each step in the search to make statistically based decisions regarding how to refine its current hypothesis. This contrasts with methods that make decisions incrementally, based on individual training examples (e.g., FIND-S or CANDIDATE-ELIMINATION). One advantage of using statistical properties of all the examples (e.g., information gain) is that the resulting search is much less sensitive to errors in individual training examples. [Advantage]
    • Machine Learning Biases • Language Bias/Restriction Bias: Restriction on the type of hypothesis to be learned. (Limits the set of hypothesis to be learned/expressed). • Preference Bias/Search Bias: A preference for certain hypothesis over others (e.g., shorter hypothesis), with no hard restriction on the hypothesis space.
    • CANDIDATE-ELIMINATION Algorithm
    • CANDIDATE-ELIMINATION Algorithm Hypothesis was assumed to be conjunction of Attributes
    • CANDIDATE-ELIMINATION Algorithm Candidate-Elimination algorithm is Language biased
    • CANDIDATE-ELIMINATION Algorithm The problem is the algorithm considers (biased) only conjunctive space. The following example requires a more expressive hypothesis space
    • Building Decision Tree Attribute: A1 Attribute value Attribute value Attribute value Output value Attribute: A2 Attribute: A3 Attribute value Attribute value Attribute value Attribute value Output value Output value Output value Output value
    • Decision Tree ID3 algorithm has Preference/Search Bias
    • ID3 Strategy for Selecting Hypothesis • Selects trees that place the attributes with highest information gain closest to the root. • Selects in favor of shorter trees over longer ones.
    • Preference Bias or Restriction Bias ? A preference bias is more desirable than a restriction bias, because it allows the learner to work within a complete hypothesis space that is assured to contain the unknown target function. In contrast, a restriction bias that strictly limits the set of potential hypotheses is generally less desirable, because it introduces the possibility of excluding the unknown target function altogether.
    • Preference Bias or Restriction Bias ? ID3 exhibits a purely preference bias and CANDIDATE-ELIMINATION a purely restriction bias, some learning systems combine both.
    • Preference Bias AND Restriction Bias ?
    • Preference Bias AND Restriction Bias ? • Task T: playing checkers • Performance measure P: % of games won in the world tournament • Training experience E: games played against itself • Target function: F : Board → R • Target function representation F'(b) = w0 + w1x1+ w2x2 + w3x3 + w4x4 + w5x5 + w6x6 A linear combination of variables (Language Bias/Restriction Bias)
    • Preference Bias AND Restriction Bias ? E(Error) ≡ ∑ < b , Ftrain ( b ) >∈ training examples (Ftrain (b) − F '(b)) 2 Preference Bias (Because weights are found based on Least Mean Square technique)
    • Issues in Decision Tree Learning • Determining how deeply to grow the decision tree • Handling continuous attributes • Choosing an appropriate attribute • Selection measure • Handling training data with missing attribute values • Handling attributes with differing costs, and improving computational efficiency
    • Occam’s Razor Occam's razor (sometimes spelled Ockham's razor) is a principle attributed to the 14th- century English logician and Franciscan friar William of Ockham. The principle states that the explanation of any phenomenon should make as few assumptions as possible, eliminating those that make no difference in the observable predictions of the explanatory hypothesis or theory.
    • Occam’s Razor This is often paraphrased as quot;All other things being equal, the simplest solution is the best.quot; In other words, when multiple competing theories are equal in other respects, the principle recommends selecting the theory that introduces the fewest assumptions and postulates the fewest entities. It is in this sense that Occam's razor is usually understood. Prefer the simplest hypothesis that fits the data
    • Why it’s called Occam’s Razor Tom M. Mitchell say’s…. Occam got this idea during shaving Wikipedia say’s….. The term razor refers to the act of shaving away unnecessary assumptions to get to the simplest explanation.
    • ID3 Strategy for Selecting Hypothesis • Selects trees that place the attributes with highest information gain closest to the root. • Selects in favor of shorter trees over longer ones.
    • Problem with Occam’s Razor Why should simplest hypothesis that fits the data is best solution. Why not second simplest or third simplest hypothesis. The size of a hypothesis is determined by the particular representation used internally by the learner. Two learners using different internal representations could therefore arrive at different hypotheses, both justifying their contradictory conclusions by Occam's razor!
    • Training and Testing For classification problems, a classifier’s performance is measured in terms of the error rate. The classifier predicts the class of each instance: if it is correct, that is counted as a success; if not, it is an error. The error rate is just the proportion of errors made over a whole set of instances, and it measures the overall performance of the classifier.
    • Training and Testing We are interested in is the likely future performance on new data, not the past performance on old data. We already know the classifications of each instance in the training set, which after all is why we can use it for training. We are not generally interested in learning about those classifications—although we might be if our purpose is data cleansing rather than prediction. So the question is, is the error rate on old data likely to be a good indicator of the error rate on new data? The answer is a resounding no—not if the old data was used during the learning process to train the classifier.
    • Training and Testing Error rate on the training set is not likely to be a good indicator of future performance.
    • Training and Testing Self-consistency Test: When training and test dataset are same The error rate on the training data is called the resubstitution error, because it is calculated by resubstituting the training instances into a classifier that was constructed from them.
    • Training and Testing Hold out Strategy: Holdout method reserves a certain amount for testing and uses the remainder for training (and sets part of that aside for validation, if required). In practical scenario we have limited number of example with us…….
    • Training and Testing K-fold Cross validation technique: In the k-fold cross-validation, the dataset was partitioned randomly into k equal-sized sets. The training and testing of each classifier were carried out k times using one distinct set for testing and other k-1 sets for training.
    • 4-Fold Cross-validation
    • 4-Fold Cross-validation ACC1 Test Dataset Training Dataset
    • 4-Fold Cross-validation ACC2 Test Dataset Training Dataset
    • 4-Fold Cross-validation ACC3 Test Dataset Training Dataset
    • 4-Fold Cross-validation ACC4 Test Dataset Training Dataset
    • 4-Fold Cross-validation ACC = (ACC1 + ACC2 + ACC3 + ACC4) / 4
    • Issues in Decision Tree Learning • Determining how deeply to grow the decision tree • Handling continuous attributes • Choosing an appropriate attribute • Selection measure • Handling training data with missing attribute values • Handling attributes with differing costs, and improving computational efficiency
    • Avoiding Overfitting in Decision Trees….. • A hypothesis is said to be over-fitting the training examples if some other hypothesis that fits the training examples less well actually performs better over the entire distribution of instances (i.e., including instances beyond the training set).
    • Overfitting H: Hypothesis Space
    • Overfitting Negative Positive example example
    • Overfitting h1 h2
    • Overfitting h1 is more accurate h1 than h2 on the training h2 examples
    • Overfitting h1 is less accurate h1 than h2 on the unseen h2 (test) examples
    • Overfitting Is h1 more accurate than h2 on training examples no yes Is h1 more accurate Is h1 more accurate than h2 on test than h2 on test examples examples yes No No yes No over-fitting Over-fitting No over-fitting Over-fitting
    • Overfitting Overfitting in decision tree learning. As ID3 adds new nodes to grow the decision tree, the accuracy of the tree measured over the training examples increases monotonically. However, when measured over a set of test examples independent of the training examples, accuracy first increases, then decreases.
    • Overfitting in Decision Tree Overfitting in decision tree learning. As ID3 adds new nodes to grow the decision tree, the accuracy of the tree measured over the training examples increases monotonically. However, when measured over a set of test examples independent of the training examples, accuracy first increases, then decreases.
    • Why Overfitting Happens in Decision Tree Learning? • Presence of error in the training examples. (In general in machine learning) • When small numbers of examples are associated with leaf node.
    • Presence of Error and Over-fitting
    • Presence of Error and Over-fitting
    • Presence of Error and Over-fitting More Complex Tree depth is more
    • Presence of Error and Over-fitting
    • How to avoid Overfitting… • Stop growing the tree earlier, before it reaches the point where it perfectly classifies the training data • Allow the tree to overfit the data, and then post-prune the tree.
    • How to avoid Overfitting… • Post-pruning overfit trees has been found to be more successful in practice. This is due to the difficulty in the first approach of estimating precisely when to stop growing the tree.
    • How to avoid Overfitting… • Regardless of whether the correct tree size is found by stopping early or by post- pruning, a key question is what criterion is to be used to determine the correct final tree size.
    • Determining correct final tree size • Use a separate set of examples for training and testing. [Training and Validation] <for pruning method> • Use all the available data for training, but apply a statistical test (for e.g., Chi-square test) to estimate whether expanding (or pruning) a particular node is likely to produce an improvement beyond the training set. <for pruning method> • Use an explicit measure of the complexity for encoding the training examples and the decision tree, halting growth of the tree when this encoding size is minimized. This approach, based on a heuristic called the Minimum Description Length principle (MDL).
    • Pruning Methods • Reduced-error pruning (Quinlan 1987) • Rule post-pruning (Quinlan 1993)
    • Reduced Error Pruning • Pruning a decision node consists of removing the subtree rooted at that node, making it a leaf node, and assigning it the most common classification of the training examples affiliated with that node. • Nodes are removed only if the resulting pruned tree performs no worse than-the original over the validation set.
    • Reduced Error Pruning
    • Reduced Error Pruning
    • Drawback of Training and Validation Method Using a separate set of data to guide pruning is an effective approach provided a large amount of data is available. The major drawback of this approach is that when data is limited.
    • Rule Post-Pruning In practice, it is one quite successful method for finding high accuracy hypotheses in post-pruning of decision tree.
    • Rule Post-Pruning (Step 1) 1
    • Rule Post-Pruning (Step 2) 2 1: IF (Outlook = sunny and Temperature = Hot) THEN PlayTennis = No 2: IF (Outlook = sunny and Temperature = Cold) THEN PlayTennis = Yes 3: IF (Outlook = sunny and Temperature = Mild and Humidity=High) THEN PlayTennis = No 4: IF (Outlook = sunny and Temperature = Mild and Humidity=Normal) THEN PlayTennis = Yes 5: IF (Outlook = overcast) THEN PlayTennis = Yes 6: IF (Outlook = rain and Wind = Strong) THEN PlayTennis = No 7: IF (Outlook = rain and Wind = Weak) THEN PlayTennis = Yes
    • Rule Post-Pruning (Step 3) 3 1: IF (Outlook = sunny and Temperature = Hot) THEN PlayTennis = No IF (Outlook = sunny and Temperature = Hot) THEN PlayTennis = No IF (Outlook = sunny) THEN PlayTennis = No Test Dataset (Validation examples) IF (Temperature = Hot) THEN PlayTennis = No
    • Rule Post-Pruning (Step 3) 3 IF (Outlook = sunny and Temperature = Hot) THEN PlayTennis = No Acc1 IF (Outlook = sunny) THEN PlayTennis = No Acc2 Test Dataset (Validation Acc3 examples) IF (Temperature = Hot) THEN PlayTennis = No If Acc3 > Acc2 & Acc1 1: IF (Outlook = sunny and Temperature = Hot) THEN PlayTennis = No IF (Temperature = Hot) THEN PlayTennis = No
    • Rule Post-Pruning (Step 4) 4 S1: Acc1 R1: Acc1 S2: Acc2 R2: Acc2 Sort rules in descending order S3: Acc3 R3: Acc3 of their accuracy on test S4: Acc4 R4: Acc4 dataset or validation examples . . . . . . S11: Acc11 R11: Acc11 S12: Acc12 R12: Acc12 S13: Acc13 R13: Acc13 S14: Acc14 R14: Acc14 S1: Acc1 >= S2: Acc2 >= S3: Acc3 >= S4: Acc4 >= … >= S11: Acc11 >= S12: Acc12 >= S13: Acc13 >= S14: Acc14
    • Handling Continuous-Valued Attribute
    • Handling Continuous-Valued Attribute
    • Handling Continuous-Valued Attribute We have dynamically defining new discrete valued attributes so that it partition the continuous attribute value into a discrete set of intervals.
    • Alternative Measures for Selecting Attributes There is a natural bias in the information gain measure that favors attributes with many values over those with few values. Consider the attribute Date, which has a very large number of possible values (e.g., March 11,2008). If we were to add this as a attribute to the data, it would have the highest information gain of any of the attributes. This is because Date alone perfectly predicts the target attribute over the training data. Thus, it would be selected as the decision attribute for the root node of the tree and lead to a (quite broad) tree of depth one, which perfectly classifies the training data. However, this decision tree would fare poorly on subsequent examples, because it is not a useful predictor despite the fact that it perfectly separates the training data.
    • Alternative Measures for Selecting Attributes What is wrong with the attribute Date? It has so many possible values that it is bound to separate the training examples into very small subsets. Because of this, it will have a very high information gain relative to the training examples, despite being a very poor predictor of the target function over unseen instances. One way to avoid this difficulty is to select decision attributes based on some measure other than information gain. One alternative measure that has been used successfully is the gain ratio (Quinlan 1986). The gain ratio measure penalizes attributes such as Date by incorporating a term, called split information, that is sensitive to how broadly and uniformly the attribute splits the data.
    • Alternative Measures for Selecting Attributes What is wrong with the attribute Date? It has so many possible values that it is bound to separate the training examples into very small subsets. Because of this, it will have a very high information gain relative to the training examples, despite being a very poor predictor of the target function over unseen instances. One way to avoid this difficulty is to select decision attributes based on some measure other than information gain. One alternative measure that has been used successfully is the gain ratio (Quinlan 1986). The gain ratio measure penalizes attributes such as Date by incorporating a term, called split information, that is sensitive to how broadly and uniformly the attribute splits the data.
    • Alternative Measures for Selecting Attributes where S1 through Sc, are the c subsets of examples resulting from partitioning S by the c-valued attribute A. Splitlnformation is actually the entropy of S with respect to the values of attribute A. This is in contrast to our previous uses of entropy, in which we considered only the entropy of S with respect to the target attribute whose value is to be predicted by the learned tree.
    • Alternative Measures for Selecting Attributes The Splitlnformation term discourages the selection of attributes with many uniformly distributed values. For example, consider a collection of n examples that are completely separated by attribute A (e.g., Date). In this case, the Splitlnformation value will be logn. In contrast, a boolean attribute B that splits the same n examples exactly in half will have Splitlnformation of 1. If attributes A and B produce the same information gain, then clearly B will score higher according to the Gain Ratio measure.
    • Handling Missing Attributes In certain cases, the available data may be missing values for some attributes. For example, in a medical domain in which we wish to predict patient outcome based on various laboratory tests, it may be that the lab test Blood-Test-Result is available only for a subset of the patients. In such cases, it is common to estimate the missing attribute value based on other examples for which this attribute has a known value.
    • Handling Missing Attributes • One strategy for dealing with the missing attribute value is to assign it the value that is most common among training examples at node n. • Alternatively, we might assign it the most common value among examples at node n that have the classification c(x) A more complex procedure is to assign a probability to each of the possible values of A rather than simply assigning the most common value to A(x). These probabilities can be estimated again based on the observed frequencies of the various values for A among the examples at node n. This method for handling missing attribute values is used in C4.5 (Quinlan 1993).
    • Handling Attributes with Different Cost In some learning tasks the instance attributes may have associated costs. For example, in learning to classify medical diseases we might describe patients in terms of attributes such as Temperature, BiopsyResult, Pulse, BloodTestResults, etc. These attributes vary significantly in their costs, both in terms of monetary cost and cost to patient comfort. In such tasks, we would prefer decision trees that use low-cost attributes where possible, relying on high-cost attributes only when needed to produce reliable classifications.
    • Handling Attributes with Different Cost ID3 can be modified to take into account attribute costs by introducing a cost term into the attribute selection measure. For example, we might divide the Gain by the cost of the attribute, so that lower-cost attributes would be preferred. However, such cost-sensitive measures do not guarantee finding an optimal cost-sensitive decision tree, they do bias the search in favor of low-cost attributes. Gain( S , A ) Cost( A )
    • Handling Attributes with Different Cost Tan and Schlimmer (1990) and Tan (1993) describe one such approach and apply it to a robot perception task in which the robot must learn to classify different objects according to how they can be grasped by the robot's manipulator. In this case the attributes correspond to different sensor readings obtained by a movable sonar on the robot. Attribute cost is measured by the number of seconds required to obtain the attribute value by positioning and operating the sonar. They demonstrate that more efficient recognition strategies are learned, without sacrificing classification accuracy, by replacing the information gain attribute selection measure by the following measure.
    • Handling Attributes with Different Cost Nunez (1988) describes a related approach and its application to learning medical diagnosis rules. Here the attributes are different symptoms and laboratory tests with differing costs. His system uses a somewhat different attribute selection measure