Typical applications include virtual sensing, electricity load forecasting, and algorithmic trading. Regression techniques predict continuous responses-for example, hard-to-measure physical quantities such as battery state-of-charge, electricity load on the grid, or prices of financial assets. ![]() The most common algorithms for performing classification can be found here. In image processing and computer vision, unsupervised pattern recognition techniques are used for object detection and image segmentation. For example, applications for hand-writing recognition use classification to recognize letters and numbers. Use classification if your data can be tagged, categorized, or separated into specific groups or classes. ![]() Typical applications include medical imaging, speech recognition, and credit scoring. Classification models classify input data into categories. ![]() Supervised learning uses classification and regression techniques to develop machine learning models.Ĭlassification techniques predict discrete responses-for example, whether an email is genuine or spam, or whether a tumor is cancerous or benign. Use supervised learning if you have known data for the output you are trying to predict. A supervised learning algorithm takes a known set of input data and known responses to the data (output) and trains a model to generate reasonable predictions for the response to new data. Supervised machine learning builds a model that makes predictions based on evidence in the presence of uncertainty.
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