EasyFit - v1.0: Perform Linear & Polynomial Regression Analysis with Transformation by Just 1-Click!

Опубликовано: 29 Июль 2026
на канале: MATLAB Apps
156
2

Perform Linear and Polynomial Regression Analysis with Transformation by Just 1-Click | MATLAB App

To download it, please visit its page on our official website:
https://matlab-apps.com/2023/08/27/ea...

In this video, a new classical machine learning app is presented. We call it "Polynomial Regression EasyFit". This application is highly user-friendly, which can give you the power and ability to generate your ordinary/simple linear and polynomial regression models by just one click. The user can increase the polynomial order up to the 9th degree.

The data can be imported from many spreadsheet formats; including .xls, .xlsx, .xlsb, .xlsm, and .csv files. You can then select any column to represent the predictor and the other one will automatically configured to represent the target or response of the model. The entries can also be modified manually or even transformed, either on y or x, before initiating the the app and starting your regression analysis if you wish!

The program supports full polynomial models up to the 9th order. To avoid facing overfitting phenomenon, the app offers you to split the full dataset into two subsets; train subset to generate the LR model and test subset to evaluate the performance of the regression model using new instances than those used in the training phase.

Also, to have repeatable results, which is very useful for comparison studies, you can easily achieve that by turning a dedicated switch ON and select the portion or percentage of the test subset, while the remaining portion will be assigned to the other subset automatically.

If you want to see the full-detailed analysis (including many plots, residuals, diagnostics, coefficient covariance, observation info, etc), then you need to check the corresponding box before running your regression analysis.

Please note that this video presents the final product. You can download the project and unlock its full potential.