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Lect8 Classification & prediction
- 2. Classification and Prediction • What is classification? What is prediction? • Issues regarding classification and prediction • Classification by decision tree induction
- 3. • Classification: – predicts categorical class labels – 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 • Typical Applications – credit approval – target marketing – medical diagnosis – treatment effectiveness analysis Classification vs. Prediction
- 4. Classification Learning: Definition l Given a collection of records (training set) – Each record contains a set of attributes, one of the attributes is the class l Find a model for the class attribute as a function of the values of the other attributes l Goal: previously unseen records should be assigned a class as accurately as possible – Use test set to estimate the accuracy of the model – Often, the given data set is divided into training and test sets, with training set used to build the model and test set used to validate it.
- 5. Illustrating Classification Learning Apply Model Induction Deduction Learn Model Model Tid Attrib1 Attrib2 Attrib3 Class 1 Yes Large 125K No 2 No Medium 100K No 3 No Small 70K No 4 Yes Medium 120K No 5 No Large 95K Yes 6 No Medium 60K No 7 Yes Large 220K No 8 No Small 85K Yes 9 No Medium 75K No 10 No Small 90K Yes 10 Tid Attrib1 Attrib2 Attrib3 Class 11 No Small 55K ? 12 Yes Medium 80K ? 13 Yes Large 110K ? 14 No Small 95K ? 15 No Large 67K ? 10 Test Set Learning algorithm Training Set
- 6. Examples of Classification Task l Predicting tumor cells as benign or malignant l Classifying credit card transactions as legitimate or fraudulent l Classifying secondary structures of protein as alpha-helix, beta-sheet, or random coil l Categorizing news stories as finance, weather, entertainment, sports, etc.
- 7. Classification - A Two-Step Process • Model construction: describing a set of predetermined classes – Building the Classifier or Model – Each tuple/sample is assumed to belong to a predefined class, as determined by the class label attribute – The set of tuples used for model construction: training set – The model is represented as classification rules, decision trees, or mathematical formulae • Model usage: for classifying future or unknown objects – Using Classifier for Classification – Estimate accuracy of the model • The known label of test sample is compared with the classified result from the model • Accuracy rate is the percentage of test set samples that are correctly classified by the model • Test set is independent of training set, otherwise over-fitting will occur
- 8. Classification Process (1): Model Construction Example: Loan application The data classification process: Learning: Training data are analyzed by a classification algorithm. Here, the class label attribute is loan decision, and the learned model or classifier is represented in the form of classification rules.
- 9. Classification Process (2): Use the Model in Prediction Classification: Test data are used to estimate the accuracy of the classification rules. If the accuracy is considered acceptable, the rules can be applied to the classification of new data tuples.
- 10. Supervised vs. Unsupervised Learning • Supervised learning (classification) – Supervision: The training data (observations, measurements, etc.) are accompanied by labels indicating the class of the observations – New data is classified based on the training set • Unsupervised learning (clustering) – The class labels of training data is unknown – Given a set of measurements, observations, etc. with the aim of establishing the existence of classes or clusters in the data
- 11. Issues (1): Data Preparation • Data cleaning – Preprocess data in order to reduce noise and handle missing values • Relevance analysis (feature selection) – Remove the irrelevant or redundant attributes • Data transformation – Generalize and/or normalize data
- 12. Issues (2): Evaluating Classification Methods • Predictive accuracy • Speed and scalability – time to construct the model – time to use the model • Robustness – handling noise and missing values • Scalability – efficiency in disk-resident databases • Interpretability: – understanding and insight provded by the model • Goodness of rules – decision tree size – compactness of classification rules
- 13. The problem • Given a set of training cases/objects and their attribute values, try to determine the target attribute value of new examples. – Classification – Prediction • Use a decision tree to predict categories for new events. • Use training data to build the decision tree. New Events Decision Tree Category Training Events and Categories