Ordinal regression is a type of statistical learning algorithm used to make predictions in data analysis. It is widely used to analyze datasets with target values that are either integer numbers, categories, or in some situations continuous values. It is a supervised learning technique which predicts the likelihood of a categorical target variable being in one category from a predefined set of categories.
Ordinal regression is commonly used in machine learning applications such as predicting credit card fraud or medical diagnosis. It can also be used for research in areas like medicine and psychology. The algorithm works by finding a linear or polynomial function that best separates the input data into various output categories.
For instance, the ordinal regression algorithm could be used to predict adult-child relationships in a dataset that incorporates factors such as age, gender, location, education, and job type. Each of the data points could then be categorized into different categories such as mother-child, father-child, aunt-nephew, etc. The ordinal regression algorithm will then output the most likely adult-child relationship based on the data inputted.
Ordinal regression is also used in market research to better understand consumer preferences. It has been employed in forecasting & predictions in sales, analytical decision making, and even product placement in stores.
The most popular algorithms for ordinal regression include logistic regression, decision tree classifiers, neural networks, support vector machines, and rank-based methods. Logistic regression is the most common algorithm used, as it is robust to outliers and can handle multiple categories, BUT all the alternatives also have specific advantages in certain scenarios.
