Data Science & Machine Learning - RMSE & R-Squared - DIY- 13 -of-50

Data Science & Machine Learning - RMSE & R-Squared - DIY- 13 -of-50
Do it yourself Tutorial
by
Bharati DW Consultancy
cell: [phone] (Cell & Whatsapp)

RMSE / RMSD
The root-mean-square deviation (RMSD) or root-mean-square error (RMSE) is a very commonly used measure of the differences between predicted values by a model and the actual values seen in the data.
The square root of the mean/average of the square of all of the error.
It compares the forecasting errors of various models for a target variable.
RMSE (angle brace)- sqrt(mean((predicted – actual) ^2));

R-Squared
R-squared is a statistical measure of how close the data are to the fitted regression line. It is also known as the coefficient of determination, or the coefficient of multiple determination for multiple predicted values.
R-squared is conveniently scaled between 0 and 1, whereas RMSE is not scaled to any particular values.
You would want R-Squared closer to 1.

Hands On – R Machine Learning Ex-10
Implement Simple & Multiple Linear Regression Model for target variable - Spend using predictor variables Age, Income, Job, Auto Loan Indicator, Gender, Marital Status.
Note RMSE for each additional variable.


Data Science & Machine Learning - Getting Started - DIY- 1 -of-50
Data Science & Machine Learning - R Data Structures - DIY- 2 -of-50
Data Science & Machine Learning - R Data Structures - Factors - DIY- 3 -of-50
Data Science & Machine Learning - R Data Structures - List & Matrices - DIY- 4 -of-50
Data Science & Machine Learning - R Data Structures - Data Frames - DIY- 5 -of-50
Data Science & Machine Learning - Frequently used R commands - DIY- 6 -of-50
Data Science & Machine Learning - Frequently used R commands contd - DIY- 7 -of-50
Data Science & Machine Learning - Installing RStudio- DIY- 8 -of-50
Data Science & Machine Learning - R Data Visualization Basics - DIY- 9 -of-50
Data Science & Machine Learning - Linear Regression Model - DIY- 10(a) -of-50
Data Science & Machine Learning - Linear Regression Model - DIY- 10(b) -of-50
Data Science & Machine Learning - Multiple Linear Regression Model - DIY- 11 -of-50
Data Science & Machine Learning - Evaluate Model Performance - DIY- 12 -of-50
Data Science & Machine Learning - RMSE & R-Squared - DIY- 13 -of-50

machine learning, data science, R programming, Deep Learning, Regression, Neural Network, R Data Structures, Data Frame, RMSE & R-Squared

[MrKnithin]

carnet package has RMSE inbuilt, could you explain how to use it ?

[vinodphogat]

which 2 variables to take for multiple linear regression plz help

[abdullahmohammed]

?hello, how can I calculate MSE for SVM

[ThePlettix]

to get the correct model performance, you have to devide your input data into independant training and test sets. Then you will train your model with the training set and predict with the test set. Comparing this predictions with the real values let you know, how your model performs to unseen data.