Linear regression is one of the most well-known and simple tools for statistics and machine learning. In this article, you can explore a linear regression algorithm, how it operates, and how you can better use it?
In this article, you can explore a linear regression algorithm, how it operates, and how you can better use it?
Linear regression (LR) is a simple yet powerful supervised learning technique. It is applied in a large number of situations.
LR determines how the input variable termed as the explanatory variables *affecting the output variable named the *response variable. It uses the best fit straight line with the smallest number of square residuals nicknamed the line of regression *or *the square least line.
The simple linear model contains only one independent variable called simple linear regression. While the multiple linear regression has more than one explanatory variable.
LR handles the study of continuous variables. It’s beneficial for companies to forecasts such as the future market trend and the salary relationship with the experience. LR used in forecasting, time series, and cause-effect relationships. The association between reckless driving and road injuries, for example.
LR could be either positive or negative. A positive relationship between the two variables means that an increase in the value of one variable always increases in the value of the other variable.
On the other hand, a negative relationship between two variables means that an increase in the value of one variable means a reduction in the value of the other variable.
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In this article, I will take you through Linear Regression with PyTorch. I will simply use the PyTorch package to build a Linear Regression
An overview of the oldest supervised machine-learning algorithm, its type & shortcomings.