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The target block represents the output that we are trying to learn with our model.

After placing a target block you have to assign a feature to it; typically a single feature is used as output, be it a label (classification), a scalar (regression) or an image (autoencoders, image segmentation).

The loss function indicates the magnitude of error your model made on its prediction. Note that for some loss functions you need to set


Feature: The feature assigned to the Target block.

Loss function: The loss function to be used in training (see the documentation for loss functions).