Supervised learning is a crucial aspect of machine learning, and it refers to the process of training algorithms on labeled data in order to make predictions about new, unseen data. The goal of supervised learning is to learn the underlying relationship between the input variables (also known as independent variables or features) and the output variable (also known as the dependent variable or label). This learned relationship can then be used to make predictions about the output variable based on new input data.
Linear models are a subset of supervised learning algorithms that model the relationship between the input variables and the output variable as a straight line. This line is represented by an equation, known as the regression line, that predicts the value of the dependent variable based on the values of the independent variables. The coefficients in the regression line represent the importance of each independent variable in predicting the dependent variable, and these coefficients can be determined through a process known as parameter estimation.
Linear Regression is a popular and widely used linear model that is used to model the relationship between a dependent variable and one or more independent variables. Simple Linear Regression is used when there is only one independent variable, while Multiple Linear Regression is used when there are multiple independent variables.
In addition to Linear Regression, there are other linear models that can be used for supervised learning, including Logistic Regression, Polynomial Regression, and Ridge Regression. These models have different assumptions and applications, and they can be used to model different types of relationships between the dependent and independent variables.
It is also important to understand the assumptions that are made by linear models, including the assumptions of linearity, independence, homoscedasticity, and normality. These assumptions help to ensure that the regression line is a good representation of the relationship between the dependent and independent variables. However, when these assumptions are violated, linear models may not be the best choice and alternative models, such as non-linear models, may need to be used.
