## 4 Non-Ensemble-Based Methods
### Random Forest Classifier
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Algorithm** & **Tuned Parameter** & **Search Range** & **Best P** & **Dataset** \\ \hline \multirow{4}{*}{Gradient boosting} & \multirow{4}{*}{Number of estimators} & [100; 150; 200; 250; 300; 400; 500; 600; 800; 1000; 1200] & & IRIS, Car, Counterceptive & \\  & & [100; 150; 200; 250; 300; 400; 500; 600; 800; 100; 1200] & 100 & Used Method, Page\_Block, User\_Knowledge, Vehicle, Wine, Volcanoes, Wall\_Following, Nursey \\ \hline \multirow{4}{*}{Craft} & \multirow{4}{*}{Learning rate} & [0.01; 0.02; 0.5; 0.1; 0.2; 0.25; 0.3; 0.4; 0.5] & 0.5 & \\ \cline{1-1} \cline{2-5}  & Min\_samples\_split & [2; 3; 4; 5; 6; 8; 10; 15] & 2 & \\ \hline \multirow{4}{*}{Random forest} & \multirow{4}{*}{Number\_of\_estimators} & [3; 4; 5; 6; 7; 8; 9; 10; 12; 15] & 4 & \\ \cline{1-1} \cline{2-5}  & & \multirow{2}{*}{Number\_of\_estimators} & [50, 120; 150; 200; 220; 250; 300; 350; 600; 700] & 200 & \\ \cline{1-1} \cline{2-5}  & & \multirow{2}{*}{Max\_features} & [’log2’;'sqrt’; 'all’] & all \\ \hline \multirow{4}{*}{Decision Tree} & \multirow{4}{*}{Min\_samples\_leaf} & [1; 2; 3; 4; 5; 6; 7; 8; 9; 10] & 2 & \\ \cline{1-1} \cline{2-5}  & & \multirow{2}{*}{’max\_features’:} & [’auto’,'sqrt’, 'log2’, Non] & None \\ \cline{1-1} \cline{2-5}  & & \multirow{2}{*}{Criterion} & \multirow{2}{*}{’gini’,’entropy’} & \multirow{2}{*}{gini} \\ \cline{1-1} \cline{2-5}  & & \multirow{2}{*}{max\_depth} & 1, 20, 2 & \\ \cline{1-1} \cline{2-5}  & & \multirow{2}{*}{Splitter} & best’, ‘random’ & best \\ \cline{1-1} \cline{2-5}  & & \multirow{2}{*}{’min\_samples\_split’:} & [2, 5, 10], & 5 & \\ \cline{1-1} \cline{2-5}  & & \multirow{2}{*}{’min\_samples\_leaf’:} & [1, 2, 4, 10], & 1 & \\ \cline{1-1} \cline{2-5} KNN & & \multirow{2}{*}{n\_neighbors’:} & \multirow{2}{*}{np.arange (1, 15),} & 5 & \\ \cline{1-1} \cline{2-5}  & & \multirow{2}{*}{weights’:} & [’uniform’, ‘distance’], & Uniform \\ \hline \multirow{4}{*}{RBF support vector machine} & \multirow{4}{*}{Gamma} & [1, 3, 5] & 3 & \\  & & & \(2\times 10^{-15},2\times 10^{-13}\), & & \\  & & & \(2\times 10^{-11},2\times 10^{-9},2\times 10^{-7}\), & & \\  & & & \(2\times 10^{-5},2\times 10^{-3},2\times 10^{-1}\), & 100 & \\  & & & \(2\times 10^{1},2\times 10^{31},0.1,0.01\), & & \\  & & & \(0.001,1\), \(10\), \(50\), \(100\), \(200\), \(500\) & & \\ \hline \multirow{4}{*}{C} & \multirow{4}{*}{Decision\_function\_shape} & \multirow{4}{*}{[O-vs-O, O-vs-R]} & \multirow{4}{*}{O-vs-R} \\ \cline{1-1} \cline{2-5}  & & \multirow{2}{*}{Class\_weight} & Uniform, balanced & uniform \\ \hline Logistic regression & Penalty & [2; 6; 8; 12; 15; 16] & 12 & \\ \cline{1-1} \cline{2-5}  & & \multirow{2}{*}{Solver} & [’newton-cg’, ’lbfgs’, ’liblinear’, ‘sag’, ’saga’] & ‘newton-cg’ & For large datasets, ‘sag’ and ’saga’ will be used \\ \cline{1-1} \cline{2-5}  & & & \multirow{2}{*}{[O-vs-O, O-vs-R]} & \multirow{2}{*}{O-vs-R} \\ \cline{1-1} \cline{2-5}  & & & \multirow{2}{*}{Class\_weight} & Uniform, balanced & uniform \\ \hline Logistic regression & Penalty & [2; 6; 8; 12; 15; 16] & 12 & \\ \hline \multirow{4}{*}{RBF support vector machine} & \multirow{4}{*}{Gamma} & [’newton-cg’, ’lbfgs’, ’liblinear’, ‘sag’, ’saga’] & \multirow{4}{*}{100} & \multirow{4}{*}{100} \\  & & & \(2\times 10^{-1},2\times 10^{-1}\), & & \\ \cline{1-1} \cline{2-5}  & & \multirow{2}{*}{[O-vs-O, O-vs-R]} & \multirow{2}{*}{O-vs-R} \\ \cline{1-1} \cline{2-5}  & & & \(2\times 10^{-5},2\times 10^{-1},2\times 1