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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:
- Classification: Definition
- Examples of Classification Task
- Base Classifiers
- Marital Status
- General Structure of Hunt’s Algorithm
- Design Issues of Decision Tree Induction
- Methods for Expressing Test Conditions
- Binary split:
- Different ways of handling
- Finding the Best Split
- Computing Gini Index for a Collection of Nodes
- Categorical Attributes: Computing Gini Index
- Continuous Attributes: Computing Gini Index
- Where
- Problem with large number of partitions
- Advantages:
- Disadvantages: .
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Given a collection of records (training set ) – Each record is by characterized by a tuple (x,y), where x is the attribute set and y is the class label x: attribute, predictor, independent variable, input y: class, response, dependent variable, output Task: Learn a model that maps each attribute set x into one of the predefined class labels y
General Approach for Building Classification Model
Decision Tree based Methods Rule-based Methods Nearest-neighbor Naïve Bayes and Bayesian Belief Networks Support Vector Machines Neural Networks, Deep Neural Nets
Single Married Single Married Divorced Married Divorced Single Married Single
Let Dt be the set of training records that reach a node t General Procedure: If Dt contains records that belong the same class yt, then t is a leaf node labeled as yt If Dt contains records that belong to more than one class, use an attribute test to split the data into smaller subsets. Recursively apply the procedure to each subset. Dt ?
How should training records be split? Method for expressing test condition depending on attribute types Measure for evaluating the goodness of a test condition How should the splitting procedure stop? Stop splitting if all the records belong to the same class or have identical attribute values Early termination
Depends on attribute types Binary Nominal Ordinal Continuous
Divides values into two subsets Preserve order property among attribute values This grouping violates order property Test Condition for Continuous Attributes
Discretization to form an ordinal categorical attribute Ranges can be found by equal interval bucketing, equal frequency bucketing (percentiles), or clustering. Static – discretize once at the beginning Dynamic – repeat at each node Binary Decision: (A < v) or (A v) consider all possible splits and finds the best cut can be more compute intensive
Compute impurity measure (P) before splitting Compute impurity measure (M) after splitting Compute impurity measure of each child node M is the weighted impurity of child nodes
l When a node is split into partitions (children) = ( ) where, = number of records at child , = number of records at parent node .
For each distinct value, gather counts for each class in the dataset
Use Binary Decisions based on one value Several Choices for the splitting value Number of possible splitting values = Number of distinct values Each splitting value has a count matrix associated with it Class counts in each of the partitions, A ≤ v and A > v Simple method to choose best v For each v, scan the database to gather count matrix and com...
of classes is the frequency of class at node , and is the total number Maximum of log when records are equally distributed among all classes, implying the least beneficial situation for classification Minimum of 0 when all records belong to one class, implying most beneficial situation for classification Entropy based computations are quite similar...
Node impurity measures tend to prefer splits that result in large number of partitions, each being small but pure – Customer ID has highest information gain because entropy for all the children is zero
Relatively inexpensive to construct Extremely fast at classifying unknown records Easy to interpret for small-sized trees Robust to noise (especially when methods to avoid overfitting are employed) Can easily handle redundant attributes Can easily handle irrelevant attributes (unless the attributes are interacting)
Due to the greedy nature of splitting criterion, interacting attributes (that can distinguish between classes together but not individually) may be passed over in favor of other attributed that are less discriminating. Each decision boundary involves only a single attribute
Learn the definition, examples, and methods of classification in data mining. See how to build and apply decision trees, rule-based methods, nearest-neighbor, naïve Bayes, support vector machines, and neural networks.
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A lecture note for chapter 4 of Introduction to Data Mining by Tan, Steinbach, Kumar. It covers the definition, examples, and techniques of classification, such as decision trees, rule-based methods, and neural networks.
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Learn the basics of classification, a data mining task that involves predicting the class label of unlabeled instances. See examples of binary and multiclass classification problems, and how to evaluate and improve classification models.
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Aug 29, 2017 · Classification is a data mining (machine learning) technique used to predict group membership for data instances. There are several classification techniques that can be used for classification...
Classification (Data Mining Book Chapters 5 and 7) • PART ONE: Supervised learning and Classification • Data format: training and test data • Concept, or class definitions and description • Rules learned: characteristic and discriminant • Supervised learning = classification process = building a classifier. • Classification algorithms
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Learn how to use prediction rules to express knowledge for data mining classification problems. Compare different algorithms such as ID3, C4.5, genetic programming, neural networks, and ant colony algorithms.
