  In the literature, a large body of work advocates the use of log-ratio
transformation for multivariate statistical analysis of compositional data. In
contrast, few studies have looked at how data transformation changes the
efficacy of machine learning classifiers within geoscience. This letter
presents experiment results and empirical observations to further explore this
issue. The objective is to study the effects of data transformation on geozone
classification performance when machine learning (ML) classifiers/estimators
are trained using geochemical data. The training input consists of exploration
hole assay samples obtained from a Pilbara iron-ore deposit in Western
Australia, and geozone labels assigned based on stratigraphic units, the
absence or presence and type of mineralization. The ML techniques considered
are multinomial logistic regression, Gaussian na\"{i}ve Bayes, kNN, linear
support vector classifier, RBF-SVM, gradient boosting and extreme GB, random
forest (RF) and multi-layer perceptron (MLP). The transformations examined
include isometric log-ratio (ILR), center log-ratio (CLR) coupled with
principal component analysis (PCA) or independent component analysis (ICA), and
a manifold learning approach based on local linear embedding (LLE). The results
reveal that different ML classifiers exhibit varying sensitivity to these
transformations, with some clearly more advantageous or deleterious than
others. Overall, the best performing candidate is ILR which is unsurprising
considering the compositional nature of the data. The performance of pairwise
log-ratio (PWLR) transformation is better than ILR for ensemble and tree-based
learners such as boosting and RF; but worse for MLP, SVM and other classifiers.
