Briefly describe five techniques (or algorithms) that are used for classification modeling

What will be an ideal response?

• Decision tree analysis. Decision tree analysis (a machine-learning technique) is arguably the most popular classification technique in the data mining arena.
• Statistical analysis. Statistical techniques were the primary classification algorithm for many years until the emergence of machine-learning techniques. Statistical classification techniques include logistic regression and discriminant analysis.
• Neural networks. These are among the most popular machine-learning techniques that can be used for classification-type problems.
• Case-based reasoning. This approach uses historical cases to recognize commonalities in order to assign a new case into the most probable category.
• Bayesian classifiers. This approach uses probability theory to build classification models based on the past occurrences that are capable of placing a new instance into a most probable class (or category).
• Genetic algorithms. This approach uses the analogy of natural evolution to build directed-search-based mechanisms to classify data samples.
• Rough sets. This method takes into account the partial membership of class labels to predefined categories in building models (collection of rules) for classification problems.