In order to train a robust and reliable ML model and avoid issues regarding co-linearity , it is helpful to pick significant features for the training process. This can be achieved by applying PCA or PLS regression, but since the number of parameters is manageable for this work, features are picked based on operator experience and a correlation matrix instead (Figure 4). An advantage of this approach is that the results will be more interpretable compared to PCA or PLS regression, which addresses possible veracity issues as well.
The values in a correlation matrix describe the linear relationship between two parameters, where 1 and -1 indicate a perfectly linear relationship. The sign of the correlation coefficient describes the direction of the relationship: a positive sign means that the value of one parameter increases or decreases, if the other parameter increases or decreases, respectively; a negative sign describes the opposite relationship.