## 2 Materials and Methods
### Experimental Setup
\begin{table}
\begin{tabular}{c c} \hline \hline
**Classifier** & **Parameters** \\ \hline DecisionTreeClassifier & (criterion = ‘gini’, splitter = ‘best’, max\_depth = None, min\_samples\_split = 2, min\_samples\_leaf = 1, \(\text{min\_weight\_fraction\_leaf}\) = 0.0, max\_features = None, random\_state = None, max\_leaf\_nodes = None, min\_impurity\_decrease = 0.0, class\_weight = None, ccp\_alpha = 0.0) \\ \hline RandomForestClassifier & (n\_estimators = 100, criterion = ‘gini’, max\_depth = None, min\_samples\_split = 2, min\_samples\_leaf = 1, min\_weight\_fraction\_leaf = 0.0, max\_features = ‘sqrt’, max\_leaf\_nodes = None, min\_impurity\_decrease = 0.0, bootstrap = True, oob\_score = False, n\_jobs = None, random\_state = None, verbose = 0, warm\_start = False, class\_weight = None, ccp\_alpha = 0.0, max\_samples = None) \\ \hline SGDClassifier & (loss = hinge’, penalty = ‘12’, alpha = 0.0001, 11\_ratio = 0.15, fit\_intercept = True, max\_iter = 1000, tol = 0.001, shuffle = True, verbose = 0, epsilon = 0.1, n\_jobs = None, random\_state = None, learning\_rate = ‘optimal’, eta0 = 0.0, power\_t = 0.5, early\_stopping = False, validation\_fraction = 0.1, n\_iter\_no\_change = 5, class\_weight = None, warm\_start = False, average = False) \\ \hline LogisticRegression & (penalty = ‘12’, dual = False, tol = 0.0001, C = 1.0, fit\_intercept = True, intercept\_scaling = 1, class\_weight = None, random\_state = None, solver = ‘liblinear’, max\_iter = 2000, multi\_class = ‘ovr’, verbose = 0, warm\_start = False, n\_jobs = 1, 11\_ratio = None) \\ \hline GaussianNB & (priors = None, var\_smoothing = 1 \(\times\) 10\({}^{-9}\)) \\ \hline MLPClassifier & (hidden\_layer\_sizes = (100), activation = ‘relu’, solver ='sgd’, alpha = 0.0001, batch\_size = ‘auto’, learning\_rate = ‘constant’, learning\_rate\_init = 0.001, power\_t = 0.5, max\_iter = 2000, shuffle = True, random\_state = None, tol = 0.0001, verbose = False, warm\_start = False, momentum = 0.9, \\  & nesterovs\_momentum = True, early\_stopping = False, validation\_fraction = 0.1, beta\_1 = 0.9, \\  & beta\_2 = 0.999, epsilon = 1 \(\times\) 10\({}^{-8}\), n\_iter\_no\_change = 10, max\_fun = 15,000) \\ \hline XGBoost & (base\_score = 0.5, booster = ‘gbtree’, device = ‘cpu’, colsample\_pybevel = 1, colsample\_pythee = 1, colsample\_pythee = 1, gamma = 0, interaction\_constants = “, learning\_rate = 0.1, max\_data\_step = 0, \\  & max\_depth = 6, min\_child\_weight = 1, monotone\_constraints = (’0’, n\_estimators = 10, \\  & num\_parallel\_tree = 1, objective = 'binary\_logistic’, random\_state = 0, reg\_alpha = 0, reg\_lambda = 1, \\  & scale\_pos\_weight = 1, subsample = 1, sampling\_method = ‘uniform’, tree\_method = ‘auto’, \\  & scale\_pos\_weight = 1, grow\_policy = ‘deplurbise’, max\_leaves = 0, max\_bin = 256, \\  & validate\_parameters = 1, verbosity = 1, use\_rmmm = False) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The parameters of used classifiers. The Anaconda (version 2.3.2) [51] distribution of the Python language together with Spyder (version 5.2.2) [52], the Scientific Python Development Environment, which is a free integrated development environment (IDE) included with Anaconda, was used. Development and computations were performed on the 64-bit Windows 10 machine with 16 GB RAM and an Intel Core i7-7700 CPU 3.60 GHz processor.