Supervised g
The method of learning the input-output relationship using the labeled data of the model.
Surveillance learning (supervised learning) is the most common and best-understanded learning paradign that a machine learning model learns the relationship between input and output using an already-known data set. In the educational process, the model produces an estimate for each input, this prediction is compared with the actual label, and the difference in the meantime is measured with a loss function; then updates weights to reduce this difference by methods such as model, resemblance and gradient landing. The process is similar to teaching a teacher by showing the right answers to the student; the phrase "obvency" (supervision) comes here.
Surveillance learning is basically divided into two types of problems: classification is used for situations where the output is a category (e.g. identifying whether an email is spam); regression is used for situations where the output is continuously numerical value (e.g. predicting the price of a house). Image classification is based on numerous real world applications surveillance learning, such as speaking recognition, credit risk score, medical diagnosis and fine adjustment stage of large language models. The largest restriction of the method is that it requires a large amount of data labeled in quality and accurately; this labeling process is usually cost-effective and time-consuming, which promotes the development of alternative approaches such as semi-supervised and unsupervised learning.
Surveillance learning is basically divided into two types of problems: classification is used for situations where the output is a category (e.g. identifying whether an email is spam); regression is used for situations where the output is continuously numerical value (e.g. predicting the price of a house). Image classification is based on numerous real world applications surveillance learning, such as speaking recognition, credit risk score, medical diagnosis and fine adjustment stage of large language models. The largest restriction of the method is that it requires a large amount of data labeled in quality and accurately; this labeling process is usually cost-effective and time-consuming, which promotes the development of alternative approaches such as semi-supervised and unsupervised learning.