# Two-Step Classification with SVD Preprocessing of Distributed Massive Datasets in Apache Spark
## 3 Proposed Method
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset Size** & **Precision** & **Recall** & **F1 Metric** & **Time** & **Number of Trees** & **Number of Nodes** & **Depth** \\ \hline \hline \multicolumn{5}{c}{one-step classifier} \\ \hline
5.000 & 0.587 & 0.589 & 0.588 & 0:30:48 & 150 & 1050 & 2 \\ \hline
10.000 & 0.611 & 0.612 & 0.611 & 0:38:21 & 150 & 2250 & 3 \\ \hline
25.000 & 0.623 & 0.622 & 0.622 & 1:00:27 & 150 & 4646 & 4 \\ \hline
50.000 & 0.628 & 0.629 & 0.629 & 1:10:17 & 150 & 4644 & 4 \\ \hline
75.000 & 0.642 & 0.644 & 0.643 & 1:13:24 & 150 & 4636 & 4 \\ \hline
100.000 & 0.638 & 0.64 & 0.639 & 1:21:09 & 150 & 4646 & 4 \\ \hline
200.000 & 0.64 & 0.641 & 0.64 & 1:49:08 & 150 & 4650 & 4 \\ \hline \multicolumn{5}{c}{two-step classifier} \\ \hline
5.000 & 0.595 & 0.596 & 0.596 & 0:30:27 & 150 & 1050 & 2 \\ \hline
10.000 & 0.603 & 0.602 & 0.603 & 0:36:13 & 150 & 2250 & 3 \\ \hline
25.000 & 0.617 & 0.618 & 0.618 & 0:43:43 & 150 & 2250 & 3 \\ \hline
50.000 & 0.627 & 0.629 & 0.628 & 1:22:24 & 150 & 4646 & 4 \\ \hline
75.000 & 0.643 & 0.644 & 0.644 & 1:20:34 & 150 & 4648 & 4 \\ \hline
100.000 & 0.64 & 0.641 & 0.64 & 1:29:18 & 150 & 4650 & 4 \\ \hline
200.000 & 0.638 & 0.64 & 0.639 & 2:04:26 & 150 & 4650 & 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Gradient-boosted trees. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset Size** & **Precision** & **Recall** & **\(F1\) Metric** & **Time** & **Best Layer** \\ \hline \multicolumn{5}{c}{one-step classifier} \\ \hline
5.000 & 0.552 & 0.551 & 0.551 & 0.06:52 & [21, 40, 30, 20, 2] \\ \hline
10.000 & 0.569 & 0.568 & 0.568 & 0.12:53 & [21, 35, 30, 25, 20, 2] \\ \hline
25.000 & 0.552 & 0.552 & 0.552 & 0.27:51 & [21, 15, 14, 2] \\ \hline
50.000 & 0.553 & 0.553 & 0.553 & 0.52:27 & [21, 15, 14, 2] \\ \hline
75.000 & 0.563 & 0.564 & 0.564 & 1:16:54 & [21, 35, 30, 25, 20, 2] \\ \hline
100.000 & 0.601 & 0.602 & 0.602 & 1:42:20 & [21, 30, 25, 20, 15, 2] \\ \hline
200.000 & 0.645 & 0.643 & 0.644 & 3:20:58 & [21, 40, 35, 30, 25, 2] \\ \hline \multicolumn{5}{c}{two-step classifier} \\ \hline
5.000 & 0.516 & 0.518 & 0.517 & 0:06:56 & [16, 35, 25, 20, 2] \\ \hline
10.000 & 0.524 & 0.524 & 0.524 & 0:11:14 & [16, 35, 30, 25, 20, 2] \\ \hline
25.000 & 0.543 & 0.542 & 0.543 & 0:24:02 & [16, 15, 14, 2] \\ \hline
50.000 & 0.546 & 0.547 & 0.547 & 0:46:20 & [16, 25, 20, 2] \\ \hline
75.000 & 0.553 & 0.553 & 0.553 & 1:06:16 & [16, 35, 25, 20, 2] \\ \hline
100.000 & 0.563 & 0.564 & 0.564 & 1:27:21 & [16, 35, 25, 20, 2] \\ \hline
200.000 & 0.578 & 0.579 & 0.579 & 2:50:38 & [16, 40, 30, 20, 2] \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Multilayer perceptron. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset Size** & **Precision** & **Recall** & **\(F1\) Metric** & **Time** & **Depth** & **Nodes** \\ \hline \multicolumn{5}{c}{one-step classifier} \\ \hline
5.000 & 0.862 & 0.859 & 0.860 & 0:15:25 & 17 & 731 \\ \hline
10.000 & 0.923 & 0.923 & 0.923 & 0:17:21 & 16 & 1007 \\ \hline
25.000 & 0.954 & 0.954 & 0.954 & 0:21:09 & 18 & 1625 \\ \hline
50.000 & 0.966 & 0.966 & 0.966 & 0:24:44 & 25 & 2441 \\ \hline
75.000 & 0.968 & 0.968 & 0.968 & 0:39:55 & 27 & 3363 \\ \hline
100.000 & 0.981 & 0.981 & 0.981 & 0:41:43 & 27 & 3139 \\ \hline
200.000 & 0.989 & 0.989 & 0.989 & 0:57:26 & 30 & 4083 \\ \hline \multicolumn{5}{c}{two-step classifier} \\ \hline
5.000 & 0.879 & 0.881 & 0.879 & 0:14:42 & 17 & 765 \\ \hline
10.000 & 0.921 & 0.921 & 0.921 & 0:15:37 & 19 & 1013 \\ \hline
25.000 & 0.955 & 0.955 & 0.955 & 0:18:39 & 26 & 1773 \\ \hline
50.000 & 0.976 & 0.976 & 0.976 & 0:21:54 & 27 & 2299 \\ \hline
75.000 & 0.974 & 0.974 & 0.974 & 0:34:25 & 24 & 2839 \\ \hline
100.000 & 0.984 & 0.984 & 0.984 & 0:36:31 & 26 & 2951 \\ \hline
200.000 & 0.991 & 0.991 & 0.991 & 0:52:08 & 30 & 3861 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Decision tree.