Linear Regression & Logistic Regression

 

 




Regression

In Regression, we compass a graph between the variables which stylish fit the given data points. The machine literacy model can deliver prognostications regarding the data. In naïve words, “Regression shows a line or wind that passes through all the data points on a target-predictor graph in such a way that the perpendicular distance between the data points and the retrogression line is minimal.” It's used basically for vaticination, soothsaying, time series modelling, and determining the unproductive- effect relationship between variables.

 

Linear Regression

 

In simple linear regression there is a single input variable (x). In multiple linear regression there is more than one input variable. The linear regression model describes the relationship within the variables with sloped straight line.

 

Linear regression is a statistical regression method used for predictive analysis. Linear regression shows the relationship between the continuous variables. It shows the linear relationship between the independent variable (X-axis) and the dependent variable (Y-axis).

 

Linear regression was developed in the field of statistics and is studied as a model for understanding the relationship between input and affair numerical variables, but has been espoused by machine literacy. It's both a statistical algorithm and a machine learning algorithm.

 

Logistic Regression

 

-Logistic regression is the applicable retrogression analysis to conduct when the dependent variable is dichotomous (double). Like all retrogression analyses, the logistic retrogression is a prophetic analysis. Logistic regression is used to describe data and to explain the relationship between one dependent double variable and one or further nominal, ordinal, interval or rate- position independent variables.

A logistic regression model predicts a dependent data variable by assaying the relationship between one or further being independent variables. For Example -Who will win football match between two teams.

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