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Fall 20041 Supervised Learning. Fall 20042 Introduction Key idea Known target concept (predict certain attribute) Find out how other attributes can be.

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Presentation on theme: "Fall 20041 Supervised Learning. Fall 20042 Introduction Key idea Known target concept (predict certain attribute) Find out how other attributes can be."— Presentation transcript:

1 Fall Supervised Learning

2 Fall Introduction Key idea Known target concept (predict certain attribute) Find out how other attributes can be used Algorithms Rudimentary Rules (e.g., 1R) Statistical Modeling (e.g., Na ï ve Bayes) Divide and Conquer: Decision Trees Instance-Based Learning Neural Networks Support Vector Machines

3 Fall Rule Generate a one-level decision tree One attribute Performs quite well! Basic idea: Rules testing a single attribute Classify according to frequency in training data Evaluate error rate for each attribute Choose the best attribute That s all folks!

4 Fall The Weather Data (again)

5 Fall Apply 1R AttributeRulesErrorsTotal 1outlooksunny no2/54/14 overcast yes0/4 rainy yes2/5 2temperaturehot no2/45/14 mild yes2/6 cool no3/7 3humidityhigh no3/74/14 normal yes2/8 4windyfalse yes2/85/14 true no3/6

6 Fall Other Features Numeric Values Discretization : Sort training data Split range into categories Missing Values Dummy attribute

7 Fall Na ï ve Bayes Classifier Allow all attributes to contribute equally Assumes All attributes equally important All attributes independent Realistic? Selection of attributes

8 Fall Bayes Theorem Hypothesis Evidence Conditional probability of H given E Prior Posterior Probability

9 Fall Maximum a Posteriori (MAP) Maximum Likelihood (ML)

10 Fall Classification Want to classify a new instance (a 1, a 2, …, a n ) into finite number of categories from the set V. Bayesian approach: Assign the most probable category v MAP given (a 1, a 2, …, a n ). Can we estimate the probabilities from the training data?

11 Fall Na ï ve Bayes Classifier Second probability easy to estimate How? The first probability difficult to estimate Why? Assume independence (this is the na ï ve bit):

12 Fall The Weather Data (yet again)

13 Fall Estimation Given a new instance with outlook=sunny, temperature=high, humidity=high, windy=true

14 Fall Calculations continued … Similarly Thus

15 Fall Normalization Note that we can normalize to get the probabilities:

16 Fall Problems …. Suppose we had the following training data: Now what?

17 Fall Laplace Estimator Replace estimates with

18 Fall Numeric Values Assume a probability distribution for the numeric attributes density f(x) normal fit a distribution (better) Similarly as before

19 Fall Discussion Simple methodology Powerful - good results in practice Missing values no problem Not so good if independence assumption is severely violated Extreme case: multiple attributes with same values Solutions: Preselect which attributes to use Non-na ï ve Bayesian methods: networks

20 Fall Decision Tree Learning Basic Algorithm: Select an attribute to be tested If classification achieved return classification Otherwise, branch by setting attribute to each of the possible values Repeat with branch as your new tree Main issue: how to select attributes

21 Fall Deciding on Branching What do we want to accomplish? Make good predictions Obtain simple to interpret rules No diversity (impurity) is best all same class all classes equally likely Goal: select attributes to reduce impurity

22 Fall Measuring Impurity/Diversity Lets say we only have two classes: Minimum Gini index/Simpson diversity index Entropy

23 Fall Impurity Functions Entropy Gini index Minimum

24 Fall Entropy Proportion of S classified as i Entropy is a measure of impurity in the training data S Measured in bits of information needed to encode a member of S Extreme cases All member same classification (Note: 0 · log 0 = 0) All classifications equally frequent Number of classes Training data (instances)

25 Fall Expected Information Gain All possible values for attribute a Gain(S,a) is the expected information provided about the classification from knowing the value of attribute a (Reduction in number of bits needed)

26 Fall The Weather Data (yet again)

27 Fall Decision Tree: Root Node Outlook Yes No Yes No Sunny Overcast Rainy

28 Fall Calculating the Entropy

29 Fall Calculating the Gain Select!

30 Fall Next Level Outlook No Yes No Yes Sunny Overcast Rainy Temperature

31 Fall Calculating the Entropy

32 Fall Calculating the Gain Select

33 Fall Final Tree Outlook NoYes Sunny Overcast Rainy Humidity High Normal Yes Windy NoYes True False

34 Fall What s in a Tree? Our final decision tree correctly classifies every instance Is this good? Two important concepts: Overfitting Pruning

35 Fall Overfitting Two sources of abnormalities Noise (randomness) Outliers (measurement errors) Chasing every abnormality causes overfitting Tree to large and complex Does not generalize to new data Solution: prune the tree

36 Fall Pruning Prepruning Halt construction of decision tree early Use same measure as in determining attributes, e.g., halt if InfoGain < K Most frequent class becomes the leaf node Postpruning Construct complete decision tree Prune it back Prune to minimize expected error rates Prune to minimize bits of encoding (Minimum Description Length principle)

37 Fall Scalability Need to design for large amounts of data Two things to worry about Large number of attributes Leads to a large tree (prepruning?) Takes a long time Large amounts of data Can the data be kept in memory? Some new algorithms do not require all the data to be memory resident

38 Fall Discussion: Decision Trees The most popular methods Quite effective Relatively simple Have discussed in detail the ID3 algorithm: Information gain to select attributes No pruning Only handles nominal attributes

39 Fall Selecting Split Attributes Other Univariate splits Gain Ratio: C4.5 Algorithm (J48 in Weka) CART (not in Weka) Multivariate splits May be possible to obtain better splits by considering two or more attributes simultaneously

40 Fall Instance-Based Learning Classification To not construct a explicit description of how to classify Store all training data (learning) New example: find most similar instance computing done at time of classification k-nearest neighbor

41 Fall K-Nearest Neighbor Each instance lives in n-dimensional space Distance between instances

42 Fall Example: nearest neighbor xq*xq* 1-Nearest neighbor? 6-Nearest neighbor? -

43 Fall Some attributes may take large values and other small Normalize All attributes on equal footing Normalizing

44 Fall Other Methods for Supervised Learning Neural networks Support vector machines Optimization Rough set approach Fuzzy set approach

45 Fall Evaluating the Learning Measure of performance Classification: error rate Resubstitution error Performance on training set Poor predictor of future performance Overfitting Useless for evaluation

46 Fall Test Set Need a set of test instances Independent of training set instances Representative of underlying structure Sometimes: validation data Fine-tune parameters Independent of training and test data Plentiful data - no problem!

47 Fall Holdout Procedures Common case: data set large but limited Usual procedure: Reserve some data for testing Use remaining data for training Problems: Want both sets as large as possible Want both sets to be representitive

48 Fall "Smart" Holdout Simple check: Are the proportions of classes about the same in each data set? Stratified holdout Guarantee that classes are (approximately) proportionally represented Repeated holdout Randomly select holdout set several times and average the error rate estimates

49 Fall Holdout w/ Cross-Validation Cross-validation Fixed number of partitions of the data (folds) In turn: each partition used for testing and remaining instances for training May use stratification and randomization Standard practice: Stratified tenfold cross-validation Instances divided randomly into the ten partitions

50 Fall Cross Validation Train on 90% of the data Model Test on 10% of the data Error rate e 1 Train on 90% of the data Model Test on 10% of the data Error rate e 2 Fold 1 Fold 2

51 Fall Cross-Validation Final estimate of error Quality of estimate

52 Fall Leave-One-Out Holdout n-Fold Cross-Validation (n instance set) Use all but one instance for training Maximum use of the data Deterministic High computational cost Non-stratified sample

53 Fall Bootstrap Sample with replacement n times Use as training data Use instances not in training data for testing How many test instances are there?

54 Fall Bootstrap On the average e -1 n = n instances will be in the test set Thus, on average we have 63.2% of instance in training set Estimate error rate e = e test e train

55 Fall Accuracy of our Estimate? Suppose we observe s successes in a testing set of n test instances... We then estimate the success rate R success =s/ n test. Each instance is either a success or failure (Bernoulli trial w/success probability p) Mean p Variance p(1-p)

56 Fall Properties of Estimate We have E[R success ]=p Var[R success ]=p(1-p)/n test If n training is large enough the Central Limit Theorem (CLT) states that, approximately, R success ~Normal(p,p(1-p)/n test )

57 Fall Confidence Interval CI for normal CI for p Look up in table Level

58 Fall Comparing Algorithms Know how to evaluate the results of our data mining algorithms (classification) How should we compare different algorithms? Evaluate each algorithm Rank Select best one Don't know if this ranking is reliable

59 Fall Assessing Other Learning Developed procedures for classification Association rules Evaluated based on accuracy Same methods as for classification Numerical prediction Error rate no longer applies Same principles use independent test set and hold-out procedures cross-validation or bootstrap

60 Fall Measures of Effectiveness Need to compare: Predicted values p 1, p 2,..., p n. Actual values a 1, a 2,..., a n. Most common measure Mean-squared error

61 Fall Other Measures Mean absolute error Relative squared error Relative absolute error Correlation

62 Fall What to Do? Large amounts of data Hold-out 1/3 of data for testing Train a model on 2/3 of data Estimate error (or success) rate and calculate CI Moderate amounts of data Estimate error rate: Use 10-fold cross-validation with stratification, or use bootstrap. Train model on the entire data set

63 Fall Predicting Probabilities Classification into k classes Predict probabilities p 1, p 2,..., p n for each class. Actual values a 1, a 2,..., a n. No longer 0-1 error Quadratic loss function Correct class

64 Fall Information Loss Function Instead of quadratic function: where the j-th prediction is correct. Information required to communicate which class is correct in bits with respect to the probability distribution

65 Fall Occam's Razor Given a choice of theories that are equally good the simplest theory should be chosen Physical sciences: any theory should be consistant with all empirical observations Data mining: theory predictive model good theory good prediction What is good? Do we minimize the error rate?

66 Fall Minimum Description Length MDL principle: Minimize size of theory + info needed to specify exceptions Suppose trainings set E is mined resulting in a theory T Want to minimize

67 Fall Most Likely Theory Suppose we want to maximize P[T|E] Bayes' rule Take logarithms

68 Fall Information Function Maximizing P[T|E] equivilent to minimizing That is, the MDL principle! Number of bits it takes to submit the theory Number of bits it takes to submit the exceptions

69 Fall Applications to Learning Classification, association, numeric prediciton Several predictive models with 'similar' error rate (usually as small as possible) Select between them using Occam's razor Simplicity subjective Use MDL principle Clustering Important learning that is difficult to evaluate Can use MDL principle

70 Fall Comparing Mining Algorithms Know how to evaluate the results Suppose we have two algorithms Obtain two different models Estimate the error rates e (1) and e (2). Compare estimates Select the better one Problem?

71 Fall Weather Data Example Suppose we learn the rule If outlook=rainy then play=yes Otherwise play=no Test it on the following test set: Have zero error rate

72 Fall Different Test Set 2 Again, suppose we learn the rule If outlook=rainy then play=yes Otherwise play=no Test it on a different test set: Have 100% error rate!

73 Fall Comparing Random Estimates Estimated error rate is just an estimate (random) Need variance as well as point estimates Construct a t-test statistic Average of differences in error rates Estimated standard deviation H 0 : Difference = 0

74 Fall Discussion Now know how to compare two learning algorithms and select the one with the better error rate We also know to select the simplest model that has 'comparable' error rate Is it really better? Minimising error rate can be misleading

75 Fall Examples of 'Good Models' Application: loan approval Model: no applicants default on loans Evaluation: simple, low error rate Application: cancer diagnosis Model: all tumors are benign Evaluation: simple, low error rate Application: information assurance Model: all visitors to network are well intentioned Evaluation: simple, low error rate

76 Fall What's Going On? Many (most) data mining applications can be thought about as detecting exceptions Ignoring the exceptions does not significantly increase the error rate! Ignoring the exceptions often leads to a simple model! Thus, we can find a model that we evaluate as good but completely misses the point Need to account for the cost of error types

77 Fall Accounting for Cost of Errors Explicit modeling of the cost of each error costs may not be known often not practical Look at trade-offs visual inspection semi-automated learning Cost-sensitive learning assign costs to classes a priori

78 Fall Explicit Modeling of Cost Confusion Matrix (Displayed in Weka)

79 Fall Cost Sensitive Learning Have used cost information to evaluate learning Better: use cost information to learn Simple idea: Increase instances that demonstrate important behavior (e.g., classified as exceptions) Applies for any learning algorithm

80 Fall Discussion Evaluate learning Estimate error rate Minimum length principle/Occam s Razor Comparison of algorithm Based on evaluation Make sure difference is significant Cost of making errors may differ Use evaluation procedures with caution Incorporate into learning

81 Fall Engineering the Output Prediction base on one model Model performs well on one training set, but poorly on others New data becomes available new model Combine models Bagging Boosting Stacking Improve prediction but complicate structure

82 Fall Bagging Bias: error despite all the data in the world! Variance: error due to limited data Intuitive idea of bagging: Assume we have several data sets Apply learning algorithm to each set Vote on the prediction (classification/numeric) What type of error does this reduce? When is this beneficial?

83 Fall Bootstrap Aggregating In practice: only one training data set Create many sets from one Sample with replacement (remember the bootstrap) Does this work? Often given improvements in predictive performance Never degeneration in performance

84 Fall Boosting Assume a stable learning procedure Low variance Bagging does very little Combine structurally different models Intuitive motivation: Any given model may be good for a subset of the training data Encourage models to explain part of the data

85 Fall AdaBoost.M1 Generate models: Assign equal weight to each training instance Iterate: Apply learning algorithm and store model e ¬ error If e = 0 or e > 0.5 terminate For every instance: If classified correctly multiply weight by e/(1-e) Normalize weight Until STOP

86 Fall AdaBoost.M1 Classification: Assign zero weight to each class For every model: Add to class predicted by model Return class with highest weight

87 Fall Performance Analysis Error of combined classifier converges to zero at an exponential rate (very fast) Questionable value due to possible overfitting Must use independent test data Fails on test data if Classifier more complex than training data justifies Training error become too large too quickly Must achieve balance between model complexity and the fit to the data

88 Fall Fitting versus Overfitting Overfitting very difficult to assess here Assume we have reached zero error May be beneficial to continue boosting! Occam's razor? Build complex models from simple ones Boosting offers very significant improvement Can hope for more improvement than bagging Can degenerate performance Never happens with bagging

89 Fall Stacking Models of different types Meta learner: Learn which learning algorithms are good Combine learning algorithms intelligently Decision Tree Naïve Bayes Instance-Based Level-0 Models Meta Learner Level-1 Model

90 Fall Meta Learning Holdout part of the training set Use remaining data for training level-0 methods Use holdout data to train level-1 learning Retrain level-0 algorithms with all the data Comments: Level-1 learning: use very simple algorithm (e.g., linear model) Can use cross-validation to allow level-1 algorithms to train on all the data

91 Fall Supervised Learning Two types of learning Classification Numerical prediction Classification learning algorithms Decision trees Na ï ve Bayes Instance-based learning Many others are part of Weka, browse!

92 Fall Other Issues in Supervised Learning Evaluation Accuracy: hold-out, bootstrap, cross- validation Simplicity: MDL principle Usefulness: cost-sensitive learning Metalearning Bagging, Boosting, Stacking

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