## 2 Materials and Methods
### Data Preparation  
A collection of volcanic rock geochemical data, covering the active volcanic regions (4) of the HVA and most non-active pre-Pliocene volcanic regions (32) of Greece, was retrieved from the GEOROC database [10]. The initial dataset provided consists of 6229 entries containing 181 features. The first 41 features are descriptive and cover general information about the rock samples. Most notable is the year of publication of the rock sample data and the sampling location. Initially, data reduction was performed using the Python libraries Pandas [40] and Numpy [41] in nine sequential steps (Figure 4). In the first step, all rock sample entries published before 1985 [12] were deleted because analytical methods--the way the chemical composition was analyzed--prior to this year are considered unreliable. This resulted in a new data matrix with the dimensions of 5506 \(\times\) 181. In the second step, 39 of the first 41 features were deleted. The 'LOCATION' feature was preserved, as it was essential for creating the classes in the eighth step. The 'CITATION' feature was used to select the validation data in the ninth step. In this way, the data matrix was reduced to 5506 \(\times\) 142. The first feature represents the location and the second represents the scientific paper where the sample's chemical composition was published, while the other 140 features contain the geochemical data of the rock samples. The chemical composition is expressed was the mass fraction (wt%) of the major elements, trace elements in parts per million (ppm) or parts per billion (ppb), and isotopes as ratios. In the third step, locations outside the research area were deleted, resulting in a 4153 \(\times\) 142 data matrix. Moreover, in the initial dataset, many NAN fields were present, which could be computed by the MLAs; therefore, in the fourth step, these were replaced with zeroes and, consequently, no data matrix dimension change occurred. The chemical compositions provided by GEOROC were determined using the XRF method. Depending on the methodology used, the iron oxides' mass fraction (wt%) was provided either in FeO, FeOT, Fe2O3, or Fe2O3T. Thus, in the fifth step, to calculate the mass fraction uniformly, these four features were unified into one (Fe2O3T) through the following procedure: all oxides measured as FeOT were transformed to Fe2O3T by first multiplying the wt% of FeOT's chemical composition by 1.1111. The chemical composition of FeO was multiplied by 1.1111 to obtain its chemical composition in Fe2O3, then, this  
Figure 4: Block diagram of the data preparation steps. value was added to the initial value of Fe\({}_{2}\)O\({}_{3}\), resulting in the Fe\({}_{2}\)O\({}_{3}\)T. In this way, the table's new dimensions were 4153 \(\times\) 139. In the sixth step, features where zeroes represented more than 65.29% (this value corresponds to the oxide P\({}_{2}\)O\({}_{5}\)) were deleted. This was necessary, as features beyond this threshold had zero values higher than 90%, making them useless for the training process of a ML model. This produced a 4153 \(\times\) 12 data matrix. In the seventh stage, 199 entries had to be deleted as all 10 of their geochemical data fields were equal to zero. The emerging new data matrix's dimensions were 3954 \(\times\) 12. In the eighth step, the classes were prepared based on the four volcanic groups of the HVA [1,2]. Methana along with Aigina and Parios were labeled as Class 1. Melos together with Antimelos and Kimolos were labeled as Class 2. Santorini with Christianoi and Kolumbo were labeled as Class 3, and finally, Nisyros with Giali, and Pergoussa were labeled as Class 4. In the final ninth step, 40 entries were randomly handpicked from the dataset to compile the validation dataset. This was done with the following constrains: 10 samples from each class, with each sample from a different publication and year. Furthermore, the 'CITATION' feature was removed, as it was no longer needed. This resulted in a 3914 \(\times\) 11 matrix dimension for the final dataset.