Читать книгу Predicting Heart Failure - Группа авторов - Страница 35

Supervised learning consists of two basic steps: creating a model with labeled data and testing with untagged data, the two prominent techniques in the category of supervised learning algorithms. Classification, one of the two prominent techniques in the category of supervised learning algorithms, is a supervised learning technique in which the target variable is of categorical type, while regression, the other prominent technique, is of the numerical type of the target variable. Operations are performed based on a model in which the target variable calculated from predictive variables is estimated. The purpose of classification is to assign records seen for the first time to one of the predefined categories. Identification and modeling of categories takes place with the help of training data. Training data and machine learning algorithms come together to form machine learning models. Machine learning models also match the records to the classroom that suits them best.The most important feature that distinguishes supervised learning from unsupervised learning is label information in supervised data. It is the class label in the data that provides the control. Although the output of both methods is different, the goal is to estimate the value of the output variable based on input variables.