Supervised learning is the machine learning task of inferring a function from labeled training data.
Moreover, The training data consist of a set of training examples.
In supervised learning, each example a pair consisting of an input object (typically a vector) and the desired output value (also called the supervisory signal).
A training set a set of data used in various areas of information science to discover potentially predictive relationships.
Training sets used in artificial intelligence, machine learning, genetic programming, intelligent systems, and statistics.
In all these fields, a training set has much the same role and often used in conjunction with a test set.
A test set is a set of data used in various areas of information science to assess the strength and utility of a predictive relationship.
Moreover, Test sets are used in artificial intelligence, machine learning, genetic programming, and statistics. In all these fields, a test set has much the same role.
Accuracy of classifier: Supervised learning
In the fields of science, engineering, industry, and statistics. The accuracy of a measurement system is the degree of closeness of measurements of a quantity to that quantity’s actual (true) value.
Sensitivity analysis: Supervised learning
Similarly, Local Sensitivity as correlation coefficients and partial derivatives can only use, if the correlation between input and output is linear.
Regression: Supervised learning
In statistics, regression analysis is a statistical process for estimating the relationships among variables.
Moreover, It includes many techniques for modeling and analyzing several variables. When the focus on the relationship between a dependent variable and one or more independent variables.
More specifically, regression analysis helps one understand how the typical value of the dependent variable (or ‘criterion variable’) changes when any one of the independent variables varied. Moreover, While the other independent variables held fixed.