Supervised learning is a machine learning paradigm where input objects, represented as a vector of variables, and a desired output value are used to train a model with labeled data. The training data is processed to construct a function that maps new data onto expected output values.
Supervised learning is a category of machine learning that uses labeled datasets to train algorithms to predict outcomes and recognize patterns. Unlike unsupervised learning, supervised learning algorithms are given labeled training to learn the relationship between the input and the outputs.[2]
Supervised learning in machine learning is generally divided into 2 categories: Classification and regression. The classification models consider categorical values, whereas the regression models predict continuous values.
Classification algorithms are used to group data by predicting a categorical label based on the input data. In a Binary Classification the data is divided into two different categories, in a Multiclass Classification, the algorithm makes a selection between more classes to define the target variable.[3]
Regression algorithms are used to predict a continuous value, where the algorithm detects a relationship between two or more variables. An example of a regression task might be predicting a salary based on work experience. For instance, a supervised learning algorithm would be fed inputs related to work experience (e.g., length of time, the industry or field, location, etc.) and the corresponding assigned salary amount. After the model is trained, it could be used to predict the average salary based on work experience. [2]
Supervised learning models find applications across various business scenarios:
Risk assessment: In the financial sector, supervised machine learning aids banks and other financial services in predicting the likelihood of customers defaulting on loans, thereby minimizing risks in their portfolios.
Image classification: These models are commonly employed to classify objects in images and videos. For instance, an algorithm can be trained to identify individuals in images, automatically tagging them on social media platforms.
Fraud detection: Supervised learning forms the foundation of many fraud detection systems, empowering enterprises to identify fraudulent activities. These models are trained on datasets containing both fraudulent and non-fraudulent activities, allowing them to flag suspicious activities in real time.
Recommendation systems: Online platforms and streaming services leverage supervised learning algorithms to enhance user experience through personalized recommendations. These algorithms analyze previous customer behavior and shopping history to extract valuable information about user preferences. [2]
[1] Supervised Learning, Wikipedia
[2] What is supervised learning?, Google Cloud
[3] Use cases for supervised machine learning, emeritus.org