The app itself is called “SVM EasyTrainer”, which emphasizes our allegation regarding its usefulness. It is highly user-friendly machine learning app that needs zero knowledge or experience of programming; specifically MATLAB programming codes. It can do all the basic and little advanced steps for you just by one or a few clicks!

By running the application, it can be clearly seen that the target vector can be selected from any column of the main table dedicated for the imported dataset, and the other columns will automatically be configured to represent the predictor(s) of the model. The entries can also be modified manually before starting the app if you would like that! The app accepts external datasets with any standard format; like .xls, .xlsx, .xlsb, .xlsm, and .csv spreadsheets.

From the control panel, the user can split the dataset to train and test subsets to reduce the overfitting phenomenon. The train subset is used to generate the model and the test subset is used to evaluate the performance of that model using new instances rather than those used in the training phase.

Furthermore, the user can select the evaluation metric (SAE, MSE, or RMSE) and whether if he/she want to reproduce the results; which is a very useful feature for comparison studies. After that, the user can select the kernel function (if it is polynomial, then the polynomial order can be set from 1 up to the 20th degree). Then, one of three possible solvers can be selected and the data itself can be standardize to enhance the sensitivity of the machine learning algorithm.

The plots can be fully customized as per user’s needs. For example, for the actual and predicted responses, you can select the plot style, width, color, markers (with marker size, face color, and edge color), major and minor grid (color, width, line style, transparency), etc. Not just that, you can even export that plot and open it in an external figure with the same settings! Add to that, the user can export all the tables for all the data sets and subsets, and save the SVM model with an m-file to run externally in the future using any other program or script.

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

MATLAB App Installer (closed source):

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Full MATLAB Project (open source):

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This application is highly user-friendly, which can give you the power and ability to generate your ordinary/simple linear regression as well as any polynomial regression model in your mind 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 be 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!

As we said above, 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; a train subset to generate the LR model and a test subset to evaluate the performance of the regression model using new instances than those used in the training phase.

Furthermore, 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.

MATLAB App Installer (closed source):

Download

Full MATLAB Project (open source):

Download

The post EasyFit – v1.0: Perform Linear & Polynomial Regression Analysis with Transformation by Just 1-Click! appeared first on MATLAB Apps.

Our app offers unparalleled customization options, giving users the ability to adjust every aspect of their diagrams with complete control. Whether you need to adjust the number of hidden layers or the number of neurons in each layer (including the input and output layers), our app has got you covered. You can even customize the sizes and colors of individual neurons, as well as the edge colors of the network. Additionally, the background color can be changed to fit any color scheme, and users can adjust the width and transparency of various elements such as the edges and neurons themselves.

Not only that, but users can also activate animation for their diagrams, bringing their neural network designs to life. Our app is perfect for researchers, data scientists, and anyone who needs to visualize their feed-forward neural network architecture in a unique and customizable way. Say goodbye to boring and generic diagrams, and create stunning visuals that truly represent your work with our app.

MATLAB App Installer (closed source):

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