Skip to main content

Multi Linear Regression

                                MULTI LINEAR REGRESSION

Before going into MULTI LINEAR REGRESSION first look into Linear Regression.

LINEAR REGRESSION:-It is all about getting the best line for the given data that supports linearity.

for Linear regression please check my previous post.

In Linear regression, we have only one independent variable and one dependent variable.

In Multilinear Regression, we have more than one independent variable and one dependent variable.

This is the main difference between Multilinear regression and Linear regression.


Formulae for Linear regression and Multilinear Regression is listed below:


Evaluation metrics for Multi-linear Regression problems are:
a)Mean Absolute error
b)Mean Squared error
c)Root Mean Squared Error
d).....


For Evaluation metrics I had posted another post please check it.


For the code part please check my Github

In the code part, I explained each and every line you will understand clearly.


Comments

Post a Comment

Popular posts from this blog

Loss Functions | MSE | MAE | RMSE

            Performance Metrics The various metrics used to evaluate the results of the prediction are : Mean Squared Error(MSE) Mean Absolute error(MAE) Root-Mean-Squared-Error(RMSE) Adjusted R² Mean Squared Error: Mean Squared error is one of the most used metrics for regression tasks. MSE is simply the average of the squared difference between the target value and value predicted by the regression model.  As it squares the differences and  penalizes (punish)even a small error which leads to over-estimation of how bad the model is. It is preferred more than other metrics because it is differentiable and hence can be optimized better. in the above formulae, y=actual value and ( yhat) means predicted value by the model. RMSE(Root Mean Squared Error: This is the same as MSE (Mean Squared Error) but the root of the value is considered while determining the accuracy of the model. It is preferred more in some cases because the errors are first...
Post a Comment
Read more

SUPPORT VECTOR MACHINE

                 SUPPORT VECTOR MACHINE:- Support vector machine:-it is a type of supervised learning algorithm it is used to solve both classification and regression problem. Note :- It is mostly used for classification problems. what we are going to learn in SVM: a) Support vectors b) Hyperplane c) Marginal Distance d) Linear Separable e) Non-linear separable f) support kernels NOw we will discuss everything in detail. Hyper plane:- in the above diagram, we have drawn three lines(A, B, C) separating two data points (stars and reds) The lines (A, B, C) are called Hyperplanes. Note:- “Select the hyper-plane which segregates the two classes better” i.e  above there are three hyperplanes how to select the best hyperplane? b)Marginal Distance:- When we draw a hyperplane the plane creates two new(------) dotted lines one line above the hyperplane and one line below the hyperplane line. see the below image you will get an ...
Post a Comment
Read more