DMTM 2015 - 10 Introduction to Classification
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The document discusses classification in data mining, focusing on model construction and usage for predicting categorical class labels using various methods. It includes examples related to contact lenses recommendations and weather datasets, alongside classification algorithms' performance evaluation criteria like accuracy, speed, robustness, and generalization. Additionally, it explores machine learning perspectives on hypothesis generation and inductive biases that influence model development.
DMTM 2015 - 10 Introduction to Classification
- 1. Prof. Pier Luca Lanzi Classification: Introduction Data Mining andText Mining (UIC 583 @ Politecnico di Milano)
- 2. Prof. Pier Luca Lanzi What is An Apple? 2
- 3. Prof. Pier Luca Lanzi Are These Apples?
- 4. Prof. Pier Luca Lanzi
- 5. Prof. Pier Luca Lanzi Contact Lenses Data 5 NoneReducedYesHypermetropePre-presbyopic NoneNormalYesHypermetropePre-presbyopic NoneReducedNoMyopePresbyopic NoneNormalNoMyopePresbyopic NoneReducedYesMyopePresbyopic HardNormalYesMyopePresbyopic NoneReducedNoHypermetropePresbyopic SoftNormalNoHypermetropePresbyopic NoneReducedYesHypermetropePresbyopic NoneNormalYesHypermetropePresbyopic SoftNormalNoHypermetropePre-presbyopic NoneReducedNoHypermetropePre-presbyopic HardNormalYesMyopePre-presbyopic NoneReducedYesMyopePre-presbyopic SoftNormalNoMyopePre-presbyopic NoneReducedNoMyopePre-presbyopic hardNormalYesHypermetropeYoung NoneReducedYesHypermetropeYoung SoftNormalNoHypermetropeYoung NoneReducedNoHypermetropeYoung HardNormalYesMyopeYoung NoneReducedYesMyopeYoung SoftNormalNoMyopeYoung NoneReducedNoMyopeYoung Recommended lensesTear production rateAstigmatismSpectacle prescriptionAge
- 6. Prof. Pier Luca Lanzi A Model for the Contact Lenses Data 6 If tear production rate = reduced then recommendation = none If age = young and astigmatic = no and tear production rate = normal then recommendation = soft If age = pre-presbyopic and astigmatic = no and tear production rate = normal then recommendation = soft If age = presbyopic and spectacle prescription = myope and astigmatic = no then recommendation = none If spectacle prescription = hypermetrope and astigmatic = no and tear production rate = normal then recommendation = soft If spectacle prescription = myope and astigmatic = yes and tear production rate = normal then recommendation = hard If age young and astigmatic = yes and tear production rate = normal then recommendation = hard If age = pre-presbyopic and spectacle prescription = hypermetrope and astigmatic = yes then recommendation = none If age = presbyopic and spectacle prescription = hypermetrope and astigmatic = yes then recommendation = none
- 7. Prof. Pier Luca Lanzi CPU Performance Data 7 0 0 32 128 CHMAX 0 0 8 16 CHMIN Channels PerformanceCache (Kb) Main memory (Kb) Cycle time (ns) 45040001000480209 67328000512480208 … 26932320008000292 19825660002561251 PRPCACHMMAXMMINMYCT PRP = -55.9 + 0.0489 MYCT + 0.0153 MMIN + 0.0056 MMAX + 0.6410 CACH - 0.2700 CHMIN + 1.480 CHMAX
- 8. Prof. Pier Luca Lanzi Classification vs. Prediction • Classification § Predicts categorical class labels (discrete or nominal) § Classifies data (constructs a model) based on the training set and the values (class labels) in a classifying attribute and uses it in classifying new data • Prediction § Models continuous-valued functions, i.e., predicts unknown or missing values • Applications § Credit approval § Target marketing § Medical diagnosis § Fraud detection 8
- 9. Prof. Pier Luca Lanzi classification = model building + model usage
- 10. Prof. Pier Luca Lanzi What is classification? • Classification is a two-step Process • Model construction § Given a set of data representing examples of a target concept, build a model to “explain” the concept • Model usage § The classification model is used for classifying future or unknown cases § Estimate accuracy of the model 10
- 11. Prof. Pier Luca Lanzi Classification: Model Construction 11 Classification Algorithm IF rank = ‘professor’ OR years 6 THEN tenured = ‘yes’ name rank years tenured Mike Assistant Prof 3 no Mary Assistant Prof 7 yes Bill Professor 2 yes Jim Associate Prof 7 yes Dave Assistant Prof 6 no Anne Associate Prof 3 no Training Data Classifier (Model)
- 12. Prof. Pier Luca Lanzi Classification: Model Usage 12 tenured = yes name rank years tenured Tom Assistant Prof 2 no Merlisa Associate Prof 7 no George Professor 5 yes Joseph Assistant Prof 7 yes Test Data Classifier (Model) Unseen Data Jeff, Professor, 4
- 13. Prof. Pier Luca Lanzi Evaluating Classification Methods • Accuracy § classifier accuracy: predicting class label § predictor accuracy: guessing value of predicted attributes • Speed § time to construct the model (training time) § time to use the model (classification/prediction time) • Other Criteria § Robustness: handling noise and missing values § Scalability: efficiency in disk-resident databases § Interpretability: understanding and insight provided § Other measures, e.g., goodness of rules, such as decision tree size or compactness of classification rules 13
- 14. Prof. Pier Luca Lanzi Example
- 15. Prof. Pier Luca Lanzi The Weather Dataset: Building the Model Outlook Temp Humidity Windy Play Sunny Hot High True No Overcast Hot High False Yes Rainy Cool Normal False Yes Rainy Cool Normal True No Overcast Cool Normal True Yes Sunny Cool Normal False Yes Sunny Mild Normal True Yes Overcast Mild High True Yes Overcast Hot Normal False Yes Rainy Mild High True No 15 • Write one rule like “if A=v1 then X, else if A=v2 thenY, …” to predict whether the player is going to play or not • A is an attribute; vi are attribute values; X,Y are class labels
- 16. Prof. Pier Luca Lanzi The Weather Dataset: Testing the Model Outlook Temp Humidity Windy Play Sunny Hot High False No Rainy Mild High False Yes Sunny Mild High False No Rainy Mild Normal False Yes 16
- 17. Prof. Pier Luca Lanzi Examples of Models • if outlook = sunny then no (3 / 2) if outlook = overcast then yes (0 / 4) if outlook = rainy then yes (2 / 3) correct: 10 out of 14 training examples • if outlook = sunny then yes (1 / 2) if outlook = overcast then yes (0 / 4) if outlook = rainy then no (2 / 1) correct: 8 out of 10 training examples 17
- 18. Prof. Pier Luca Lanzi The Machine Learning Perspective
- 19. Prof. Pier Luca Lanzi The Machine Learning Perspective • Classification algorithms are methods of supervised Learning • The experience E consists of a set of examples of a target concept that have been prepared by a supervisor • The task T consists of finding an hypothesis that accurately explains the target concept • The performance P depends on how accurately the hypothesis h explains the examples in E 19
- 20. Prof. Pier Luca Lanzi The Machine Learning Perspective • Let us define the problem domain as the set of instance X (for instance, X contains different different fruits) • We define a concept over X as a function c which maps elements of X into a range D or c:X→ D • The range D represents the type of concept analyzed • For instance, c: X → {isApple, notAnApple} 20
- 21. Prof. Pier Luca Lanzi The Machine Learning Perspective • Experience E is a set of x,d pairs, with x∈X and d∈D. • The task T consists of finding an hypothesis h to explain E: • ∀x∈X h(x)=c(x) • The set H of all the possible hypotheses h that can be used to explain c it is called the hypothesis space • The goodness of an hypothesis h can be evaluated as the percentage of examples that are correctly explained by h P(h) = | {x| x∈X e h(x)=c(x)}| / |X| 21
- 22. Prof. Pier Luca Lanzi Examples • Concept Learning when D={0,1} • Supervised classification when D consists of a finite number of labels • Prediction when D is a subset of Rn 22
- 23. Prof. Pier Luca Lanzi The Machine Learning Perspective on Classification • Supervised learning algorithms, given the examples in E, search the hypotheses space H for the hypothesis h that best explains the examples in E • Learning is viewed as a search in the hypotheses space 23
- 24. Prof. Pier Luca Lanzi Searching for Hypotheses • The type of hypothesis required influences the search algorithm • The more complex the representation the more complex the search algorithm • Many algorithms assume that it is possible to define a partial ordering over the hypothesis space • The hypothesis space can be searched using either a general to specific or a specific-to-general strategy 24
- 25. Prof. Pier Luca Lanzi Exploring the Hypothesis Space • General to Specific § Start with the most general hypothesis and then go on through specialization steps • Specific to General § Start with the set of the most specific hypothesis and then go on through generalization steps 25
- 26. Prof. Pier Luca Lanzi Inductive Bias • Set of assumptions that together with the training data deductively justify the classification assigned by the learner to future instances • There can be a number of hypotheses consistent with training data • Each learning algorithm has an inductive bias that imposes a preference on the space of all possible hypotheses 26
- 27. Prof. Pier Luca Lanzi Types of Inductive Bias • Syntactic Bias § Depends on the language used to represent hypotheses • Semantic Bias § Depends on the heuristics used to filter hypotheses • Preference Bias § Depends on the ability to rank and compare hypotheses • Restriction Bias § Depends on the ability to restrict the search space 27
- 28. Prof. Pier Luca Lanzi Why Are We Looking for h?
- 29. Prof. Pier Luca Lanzi Inductive Learning Hypothesis • Any hypothesis (h) found to approximate the target function (c) over a sufficiently large set of training examples will also approximate the target function (c) well over other unobserved examples. • Training § The hypothesis h is developed to explain the examples in ETrain • Testing § The hypothesis h is evaluated (verified) with respect to the previously unseen examples in ETest • The underlying hypothesis § If h explains ETrain then it can also be used to explain other unseen examples in ETest (not previously used to develop h) 29
- 30. Prof. Pier Luca Lanzi Generalization and Overfitting • Generalization § When h explains “well” both ETrain and ETest we say that h is general and that the method used to develop h has adequately generalized • Overfitting § When h explains ETrain but not ETest we say that the method used to develop h has overfitted § We have overfitting when the hypothesis h explains ETrain too accurately so that h is not general enough to be applied outside ETrain 30
- 31. Prof. Pier Luca Lanzi What are the general issues for classification in Machine Learning? • Type of training experience § Direct or indirect? § Supervised or not? • Type of target function and performance • Type of search algorithm • Type of representation of the solution • Type of inductive bias 31