Perhaps one of the most common and comprehensive statistical and machine learning algorithms are linear regression. Linear regression is used to find a linear relationship between one or more predictors. The linear regression has two types: simple regression and multiple regression (MLR). This paper discusses various works by different researchers on linear regression and polynomial regression and compares their performance using the best approach to optimize prediction and precision. Almost all of the articles analyzed in this review is focused on datasets; in order to determine a model's efficiency, it must be correlated with the actual values obtained for the explanatory variables.
Regression is a supervised learning technique that supports finding the correlation among variables. A regression problem is when the output variable is a real or continuous value.
In this article, we will understand the following concepts:
- What is a Regression?
- Types of a Regression.
- What is the mean of Linear regression and the importance of Linear regression?
- Importance of cost function and gradient descent in a Linear regression.
- Impact of different values for learning rate.
- Implement use case of Linear regression with python code.
What is a Regression ?
In Regression, we plot a graph between the variables which best fit the given data points. The machine learning model can deliver predictions regarding the data. In naïve words, “Regression shows a line or curve that passes through all the data points on a target-predictor graph in such a way that the vertical distance between the data points and the regression line is minimum.” It is used principally for prediction, forecasting, time series modeling, and determining the causal-effect relationship between variables.
Types of Regression models
- Linear Regression
- Polynomial Regression
- Logistics Regression
Linear Regression
Linear regression is a quiet and simple statistical regression method used for predictive analysis and shows the relationship between the continuous variables. Linear regression shows the linear relationship between the independent variable (X-axis) and the dependent variable (Y-axis), consequently called linear regression. If there is a single input variable (x), such linear regression is called simple linear regression. And if there is more than one input variable, such linear regression is called multiple linear regression. The linear regression model gives a sloped straight line describing the relationship within the variables.
The above graph presents the linear relationship between the dependent variable and independent variables. When the value of x (independent variable) increases, the value of y (dependent variable) is likewise increasing. The red line is referred to as the best fit straight line. Based on the given data points, we try to plot a line that models the points the best.