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It predicts the output values based on input values. It is mainly used for weather forecasting, finding the causal-effect
relationship between variables and time series modelling.
In regression tasks, there are two kinds of variables being studied: the dependent variables and the independent variables.
• Independent variables: Quantities that can be measured directly.
• Dependent variables: Quantities whose value depends on independent variables.
As the independent variable is adjusted, the level of the dependent variable will vary. The dependent variable is the
variable under study, and it is the variable that the regression model tries to predict. In the linear regression task, each
observation is made up of the value of the dependent variable and the value of the independent variable.
Regression is basically used when the dependent variable is of a continuous data type. The independent variables, on
the other hand, can be of any data type—continuous, nominal/categorical etc.
There are several types of regression analysis, which are as follows:
Random Forest Support Vector Decision Tree
Regression Regression Regression
Polynomial
Linear Regression Types of Regression
Regression
Ridge Regression Lasso Regression Logistic Regression
Linear Regression—Finding the Line
When we make a distribution in which there is an involvement of more than one variable, then such an analysis is
called Regression analysis. Regression generally focuses on predicting the value of the variable that is dependent on
the other variable. Let us consider two variables x and y.
y – Regression or Dependent Variable
x – Independent Variable or Predictor
Therefore, if we use a simple linear regression model where y
depends on x, then the regression line of y on x is:
y = mx + b + e line: y=mx+b + e
where, y (dependent) variable
• x is the independent variable. ŷ
• y is the dependent variable. (Predicted) 2
y
• m is the slope of the line. 1 y 2
(Observed)
• b is the y-intercept.
• e is the residual error and represents y(observed) - y-intercept
ŷ(predicted) or (y - ŷ ) x 1 x (independent) variable
2
2
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