Lect8 Classification & prediction

classification & prediction

1. Data Mining: Classification

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 Given a collection of records ( training set ) Each record contains a set of attributes , one of the attributes is the class Find a model for the class attribute as a function of the values of the other attributes 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

6. Examples of Classification Task Predicting tumor cells as benign or malignant Classifying credit card transactions as legitimate or fraudulent Classifying secondary structures of protein as alpha-helix, beta-sheet, or random coil 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