Introduction to Classification

 An introduction to classification in machine learning is provided. Classification is a supervised learning approach where items are categorized into discrete classes based on features and target labels. The goal is to learn the relationship between feature variables and a categorical target variable.

Classification works by using a set of training data points with target labels to determine the class label for unlabeled test cases. An example used is loan default prediction, where a bank uses customer data to predict loan default likelihood.

Classifiers can be binary (two classes) or multi-class (more than two classes). Examples of multi-class classification include predicting which medication is appropriate for a patient based on their illness response.

Classification has various business applications such as customer categorization, churn detection, and advertising campaign response prediction. It is widely used in industries for tasks like email filtering, speech recognition, and document classification.

Different types of classification algorithms include decision trees, naive Bayes, linear discriminant analysis, k-nearest neighbor, logistic regression, neural networks, and support vector machines. The video covers only a few of these algorithms.