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Supervised learning models are trained using labeled datasets, meaning the algorithm knows the correct output for each input example []. This allows the model to generalize to new data and measure its accuracy over time. The two primary subcategories within supervised learning are classification, where the output is a discrete category like "spam" or "not spam" [], and regression, which predicts a continuous value such as price or probability []. Common classification algorithms include decision trees and random forests, while linear and logistic regression are typical regression methods.
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Video summary will appear here after you start watching
Supervised learning models are trained using labeled datasets, meaning the algorithm knows the correct output for each input example []. This allows the model to generalize to new data and measure its accuracy over time. The two primary subcategories within supervised learning are classification, where the output is a discrete category like "spam" or "not spam" [], and regression, which predicts a continuous value such as price or probability []. Common classification algorithms include decision trees and random forests, while linear and logistic regression are typical regression methods.