# 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} \hline \hline
**Dataset Size** & **Precision** & **Recall** & \(F1\) **Metric** & **Time** & **Depth** & **Number of Nodes** \\ \hline \multicolumn{6}{c}{one-step classifier} \\ \hline
5.000 & 0.581 & 0.583 & 0.581 & 0:12:23 & 10 & 443 \\ \hline
10.000 & 0.594 & 0.593 & 0.593 & 0:13:33 & 7 & 215 \\ \hline
25.000 & 0.593 & 0.592 & 0.592 & 0:17:01 & 8 & 327 \\ \hline
50.000 & 0.589 & 0.59 & 0.589 & 0:32:32 & 8 & 357 \\ \hline
75.000 & 0.606 & 0.607 & 0.607 & 0:40:26 & 9 & 749 \\ \hline
100.000 & 0.602 & 0.603 & 0.602 & 0:48:19 & 9 & 745 \\ \hline
200.000 & 0.609 & 0.609 & 0.609 & 1:21:53 & 10 & 1357 \\ \hline \multicolumn{6}{c}{two-step classifier} \\ \hline
5.000 & 0.57 & 0.568 & 0.569 & 0:11:31 & 8 & 263 \\ \hline
10.000 & 0.589 & 0.589 & 0.589 & 0:13:37 & 7 & 189 \\ \hline
25.000 & 0.593 & 0.593 & 0.593 & 0:16:25 & 8 & 331 \\ \hline
50.000 & 0.593 & 0.593 & 0.593 & 0:51:13 & 8 & 353 \\ \hline
75.000 & 0.608 & 0.61 & 0.609 & 0:53:45 & 9 & 757 \\ \hline
100.000 & 0.61 & 0.61 & 0.61 & 1:05:24 & 9 & 739 \\ \hline
200.000 & 0.611 & 0.611 & 0.611 & 1:48:17 & 10 & 1391 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Decision tree. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset Size** & **Precision** & **Recall** & \(F1\) **Metric** & **Time** & **Number of Trees** & **Number of Nodes** \\ \hline \multicolumn{6}{c}{one-step classifier} \\ \hline
5.000 & 0.59 & 0.59 & 0.59 & 0:16:07 & 50 & 24,056 \\ \hline
10.000 & 0.612 & 0.609 & 0.611 & 0:21:43 & 54 & 36,924 \\ \hline
25.000 & 0.622 & 0.623 & 0.622 & 0:33:33 & 52 & 48,906 \\ \hline
50.000 & 0.615 & 0.615 & 0.615 & 1:22:39 & 36 & 40,438 \\ \hline
75.000 & 0.628 & 0.629 & 0.629 & 1:47:26 & 56 & 68,434 \\ \hline
100.000 & 0.631 & 0.633 & 0.632 & 2:15:43 & 60 & 78,488 \\ \hline
200.000 & 0.627 & 0.627 & 0.627 & 4:15:01 & 58 & 85,640 \\ \hline \multicolumn{6}{c}{two-step classifier} \\ \hline
5.000 & 0.597 & 0.596 & 0.597 & 0:16:07 & 46 & 22,682 \\ \hline
10.000 & 0.622 & 0.62 & 0.621 & 0:21:18 & 54 & 37,778 \\ \hline
25.000 & 0.62 & 0.62 & 0.62 & 0:33:02 & 60 & 57,682 \\ \hline
50.000 & 0.625 & 0.626 & 0.626 & 1:36:53 & 56 & 63,868 \\ \hline
75.000 & 0.63 & 0.632 & 0.631 & 2:06:27 & 46 & 57,202 \\ \hline
100.000 & 0.631 & 0.632 & 0.632 & 2:36:19 & 60 & 80,748 \\ \hline
200.000 & 0.628 & 0.628 & 0.628 & 4:26:49 & 56 & 84,224 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Random forest. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset Size** & **Precision** & **Recall** & **F1 Metric** & **Time** \\ \hline \hline \multicolumn{5}{c}{one-step classifier} \\ \hline
5.000 & 0.551 & 0.555 & 0.553 & 0:00:02 \\ \hline
10.000 & 0.574 & 0.572 & 0.573 & 0:00:02 \\ \hline
25.000 & 0.561 & 0.56 & 0.56 & 0:00:02 \\ \hline
50.000 & 0.563 & 0.563 & 0.563 & 0:00:02 \\ \hline
75.000 & 0.567 & 0.566 & 0.566 & 0:00:02 \\ \hline
100.000 & 0.567 & 0.567 & 0.567 & 0:00:02 \\ \hline
200.000 & 0.563 & 0.564 & 0.563 & 0:00:04 \\ \hline \multicolumn{5}{c}{two-step classifier} \\ \hline
5.000 & 0.605 & 0.606 & 0.606 & 0:00:01 \\ \hline
10.000 & 0.619 & 0.617 & 0.618 & 0:00:01 \\ \hline
25.000 & 0.616 & 0.616 & 0.616 & 0:00:01 \\ \hline
50.000 & 0.615 & 0.615 & 0.615 & 0:00:01 \\ \hline
75.000 & 0.617 & 0.617 & 0.617 & 0:00:01 \\ \hline
100.000 & 0.618 & 0.618 & 0.618 & 0:00:02 \\ \hline
200.000 & 0.620 & 0.621 & 0.620 & 0:00:04 \\ \hline \end{tabular}
\end{table} 
Table 8: Logistic regression.