Classifies object into categories using one of the following models:
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PLS-DA
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SIMCA
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Machine Learning
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Curve Separation
For more information on how to train a model see any the classification of nuts tutorials: Nut classification
Parameters
Model
Select the model created for the classification of the samples.
Category
Which category to apply the segmentation.
Classification type
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Object average spectrum
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Classifies the object by taking the average spectrum from all pixels included in the object.
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Pixel class majority
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Classifies the object by the majority of the pixels in the object.
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Weights
Only applicable when Pixel class majority is selected
Decides the importance of the pixels for the classification using pixel class majority.
Pixel prediction
✅ Includes prediction of each pixel and visualization of the pixel prediction on the object.
⬜ Do not include the prediction or visualization
Connection
Only applicable for Hierarchical classification
Set parent class for hierarchical model.
Show Train/Test column in table
Adds an additional column in table showing if the object was part of Train or Test set
✅ Add Train/Test column
⬜ Do not add Train/Test column