![]() Basic editing can also be performed using the above options.Īs we can see in the output, we have obtained an image of the moon that can be processed using the icons in the ribbon. We can use the above options in the image processing toolbox to get detailed information about our image or do pre-processing. Options Provided by Image Processing Toolbox Once we execute the above code in ‘Command Window’, we will get the ‘moonImage’ in our ‘WORKSPACE’. We will upload this dataset to ‘Image processing Toolbox’ and will explore the possible options. In this example, we will use one of the inbuilt images provided by MATLAB, ‘moon.tiff. Image Processing Toolboxīelow we will learn about image processing toolbox: Example Let us now understand the use of the Image processing toolbox using an example. Now as per our requirement, we can train this data and get a response plot, residual plot, min MSE plot using the options available. We can immediately see a response plot created by Regression Learner Toolbox. Step 8: Click on ‘Start Session’, to start analyzing the data Step 7: Now we can select the predictor variables as per our requirement Step 6: This will load all the predictor variables under the section ‘Predictors’ Step 5: From the ‘Data Set Variable’ dropdown, select the ‘newTable’ table created by us Step 4: Click on New Session in the left which will open a new window prompt Step 2: Select ‘Regression Learner Toolbox’ Once we execute the above code in ‘Command Window’, we will get the ‘newTable’ created in our ‘WORKSPACE’. NewTable = table (Cylinders, Acceleration, Displacement.
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