Error Calculation Techniques For Linear Regression
I hope you and your model are doing good !
There are various methods to calculate the accuracy of our model. In this article I’m going to cover a few of them. In this article we are not going to use python libraries to calculate accuracy of models, but instead we are going to do it from scratch. I hope you guys enjoy it.
In this article I’m going to use a user defined function to calculate the slope and intercept of a regression line. So if you haven’t read my previous article about it’s derivation then I think it’ll be for you to start with that. It won’t take more than 5 mins!!
So let’s get started!
(1) Initialize our dataset :
(2) Plot the data on scatterplot :
(3) Main function to calculate the slope and intercept :
(4) Print the slope and intercept values :
(5) Prediction function :
(6) Predicting our value :
(7) Plot the regression line :
Accuracy Of Model :
(1) Residual Sum Of Squares : (RSS)
(2) Mean Absolute Error : (MAE)
(3) Mean Squared Error : (MSE)
(4) Mean Absolute percentage Error (MAPE) :
(5) Mean Percentage Error : (MPE)
(6) Root Mean Squared Error : (RMSE)
So, that’s it for this article!
(1) Simple Linear Regression Explained With It’s Derivation:
https://youtu.be/1M2-Fq6wl4M
(2)How to Calculate The Accuracy Of A Model In Linear Regression From Scratch :
https://youtu.be/bM3KmaghclY
(3) Simple Linear Regression Using Sklearn :
https://youtu.be/_VGjHF1X9oU
Part 1 : Linear Regression From Scratch.
Part 2 : Linear Regression Line Through Brute Force.
Part 3 : Linear Regression Complete Derivation.
Part 4 : Simple Linear Regression Implementation From Scratch.
Part 5 : Simple Linear Regression Implementation Using Scikit-Learn.
If you have any additional questions; feel free to contact me shuklapratik22@gmail.com
To find more such detailed explanation, visit my blog: patrickstar0110.blogspot.com
