Data Classification

Data Classification

• Data classification is a two-step process: building a model and using the model for classification.
• The model is constructed by analyzing database tuples (samples, examples, or objects) described by attributes.
• Each tuple is assumed to belong to a predefined class, as determined by the class label attribute.

Supervised Learning

• In the first step, the model is built using a supervised learning approach, where the class label of each training sample is provided.
• The learning process is 'supervised' in that it is told to which class each training sample belongs.
• The learned model is typically represented in the form of classification rules, decision trees, or mathematical formulae.

Classification Rules

• Classification rules can be used to categorize future data samples and provide a better understanding of the database contents.
• Example: given a database of customer credit information, classification rules can be learned to identify customers as having either excellent or fair credit ratings.

Model Evaluation

• The predictive accuracy of the model is estimated using a test set of class-labeled samples.
• The holdout method is a simple technique that uses a test set of class-labeled samples to evaluate the model's accuracy.
• The accuracy of a model on a given test set is the percentage of test set samples that are correctly classified by the model.

Avoiding Overfitting

• If the accuracy of the model were estimated based on the training data set, this estimate could be optimistic since the learned model tends to overfit the data.
• Therefore, a test set is used to evaluate the model's accuracy to avoid overfitting.

Classification

• Classification is a data mining technique used to predict group membership for data instances.
• It is used to predict categorical variables, such as income bracket, which can be partitioned into multiple classes or categories.
• Popular classification techniques include decision trees and neural networks.

Classification Task

• A classification task involves examining a large set of records, each containing information on the target variable and a set of input or predictor variables.
• The goal is to classify the target variable (e.g., income brackets) based on the input variables (e.g., age, gender, and occupation).