Linear Regression is a machine learning algorithm based on supervised learning. It performs a regression task. Regression models are target prediction value based on independent variables. It is mostly used for finding out the relationship between variables and forecasting. Please refer Linear Regression for complete reference.
Let’s discuss some advantages and disadvantages of Linear Regression.
|Linear Regression is simple to implement and easier to interpret the output coefficients.||On the other hand in linear regression technique outliers can have huge effects on the regression and boundaries are linear in this technique.|
|When you know the relationship between the independent and dependent variable have a linear relationship, this algorithm is the best to use because of it’s less complexity to compared to other algorithms.||Diversely, linear regression assumes a linear relationship between dependent and independent variables. That means it assumes that there is a straight-line relationship between them. It assumes independence between attributes.|
|Linear Regression is susceptible to over-fitting but it can be avoided using some dimensionality reduction techniques, regularization (L1 and L2) techniques and cross-validation.||But then linear regression also looks at a relationship between the mean of the dependent variables and the independent variables. Just as the mean is not a complete description of a single variable, linear regression is not a complete description of relationships among variables.|
Linear Regression is a great tool to analyze the relationships among the variables but it isn’t recommended for most practical applications because it over-simplifies real-world problems by assuming a linear relationship among the variables.