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Linear Regression Pre-Read

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Linear Regression – Pre Read

Linear Regression is a linear approach to modelling the relationship between an independent variable and dependent variable. In other words, we predict the dependent variable based on the independent variable.

Regression is a measure between the mean value of one variable and a corresponding value of the other variable.

A linear regression line has an equation of the form Y = a + bX, where X is the explanatory variable and Y is the dependent variable. The slope of the line is b, and a is the intercept (the value of y when x = 0).

Advantages:

  • Linear Regression works with almost every kind of Dataset gives quite good information about the features.
  • Easier to Implement and interpret.
  • Efficient to train, prone to over fitting

Disadvantages:

  • There are few assumptions in this regression model.
  • It depends only on the mean value of the variable.
  • Linear Regression is easily affected by the outliers.
  • Data must be Independent.
  • It performs well only with the linearly separable dataset

ANUJ SINGH

Student, Data Science

NMIMS, Indore

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