linear regression in conjunction with neural network

Introduction to Linear regression Model

linear regression in conjunction with neural network

Linear regression is a basic machine learning model used to predict values based on input features.

It takes features as input and return label as output, features are the identifiers by which we can recognize a subject and label are the recognized values.

A linear regression model is often used as a base for a machine learning model which can be further used for advance learning models.

Linear regression makes a best-fit line in a graph by which it calculates MSE(Mean Squared Error) or SSE(Sum of Squared Errors).

Introduction to Best-Fit Line

A Best-fit line is a calculated line which minimizes the overall MSE/SSE, the output after fitting the best-fit line is the score of the model of how it performs.

How much error a model generates is known from the calculation of best-fit line.

Mathematical equation for best-fit line

Formula to find best fit line is – y = m(x) + b

y is the dependent variable (the value you’re trying to predict or explain),

x is the independent variable (the value you’re using to predict y),

m is the slope of the line, which represents the rate of change in y for a one unit increase in x,

b is the y-intercept, which represents the value of y when x=0.

How to find intercept: b=∑y−m∑x/n

​Graph of Linear regression

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