This toolchain will first train an LDA projection using all the training data available. Because this is a binary classification problem, 1 LDA component will be left-over after this step.

Once the LDA vector has been estimated, each sample on the training set is projected through this vector in order to compress the input data into a single score, which is then used for classification.