# 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} \hline \hline
**Dataset Size** & **Precision** & **Recall** & \(F1\) **Metric** & **Time** \\ \hline \multicolumn{6}{c}{one-step classifier} \\ \hline
5.000 & 0.609 & 0.609 & 0.609 & 0:00:02 \\ \hline
10.000 & 0.649 & 0.645 & 0.639 & 0:00:02 \\ \hline
25.000 & 0.636 & 0.634 & 0.634 & 0:00:02 \\ \hline
50.000 & 0.644 & 0.642 & 0.642 & 0:00:04 \\ \hline
75.000 & 0.644 & 0.645 & 0.644 & 0:00:05 \\ \hline
100.000 & 0.648 & 0.648 & 0.648 & 0:00:05 \\ \hline
200.000 & 0.641 & 0.639 & 0.639 & 0:00:08 \\ \hline \multicolumn{6}{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 & 0:00:03 \\ \hline
50.000 & 0.615 & 0.615 & 0.615 & 0:00:03 \\ \hline
75.000 & 0.617 & 0.617 & 0.617 & 0:00:04 \\ \hline
100.000 & 0.618 & 0.618 & 0.618 & 0:00:06 \\ \hline
200.000 & 0.620 & 0.621 & 0.620 & 0:00:08 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Logistic regression. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset Size** & **Precision** & **Recall** & \(F1\) **Metric** & \multicolumn{2}{c}{**Time**} & \multicolumn{2}{c}{**Number of Trees**} & \multicolumn{2}{c}{**Number of Nodes**} & **Depth** \\ \hline \multicolumn{7}{c}{one-step classifier} \\ \hline
5.000 & 0.680 & 0.681 & 0.680 & 0.33:54 & 150 & 1050 & 2 \\ \hline
10.000 & 0.707 & 0.707 & 0.707 & 0.45:17 & 150 & 2250 & 3 \\ \hline
25.000 & 0.701 & 0.701 & 0.701 & 1:13:27 & 150 & 2250 & 3 \\ \hline
50.000 & 0.714 & 0.714 & 0.714 & 1:24:24 & 150 & 4648 & 4 \\ \hline
75.000 & 0.723 & 0.724 & 0.723 & 1:34:58 & 150 & 4650 & 4 \\ \hline
100.000 & 0.723 & 0.723 & 0.723 & 1:42:02 & 150 & 4650 & 4 \\ \hline
200.000 & 0.725 & 0.725 & 0.725 & 2:08:07 & 150 & 4650 & 4 \\ \hline \multicolumn{7}{c}{two-step classifier} \\ \hline
5.000 & 0.654 & 0.654 & 0.654 & 0.32:13 & 150 & 1050 & 2 \\ \hline
10.000 & 0.684 & 0.684 & 0.684 & 0:40:36 & 150 & 2250 & 3 \\ \hline
25.000 & 0.681 & 0.681 & 0.681 & 1:11:34 & 150 & 4646 & 4 \\ \hline
50.000 & 0.691 & 0.691 & 0.691 & 1:14:43 & 150 & 4642 & 4 \\ \hline
75.000 & 0.693 & 0.693 & 0.693 & 1:25:17 & 150 & 4648 & 4 \\ \hline
100.000 & 0.694 & 0.694 & 0.694 & 1:34:30 & 150 & 4648 & 4 \\ \hline
200.000 & 0.695 & 0.695 & 0.695 & 1:57:28 & 150 & 4650 & 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: 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{7}{c}{one-step classifier} \\ \hline
5.000 & 0.595 & 0.595 & 0.595 & 0:06:51 & [28, 15, 14, 2] \\ \hline
10.000 & 0.641 & 0.641 & 0.641 & 0:12:55 & [28, 15, 14, 2] \\ \hline
25.000 & 0.688 & 0.688 & 0.688 & 0:27:49 & [28, 15, 14, 2] \\ \hline
50.000 & 0.711 & 0.712 & 0.711 & 0:54:28 & [28, 15, 14, 2] \\ \hline
75.000 & 0.726 & 0.726 & 0.726 & 1:17:05 & [28, 20, 15, 2] \\ \hline
100.000 & 0.726 & 0.726 & 0.726 & 1:40:54 & [28, 25, 20, 15, 2] \\ \hline
200.000 & 0.720 & 0.720 & 0.720 & 3:50:29 & [28, 25, 20, 2] \\ \hline \multicolumn{7}{c}{two-step classifier} \\ \hline
5.000 & 0.551 & 0.551 & 0.551 & 0:08:45 & [21, 35, 25, 20, 2] \\ \hline
10.000 & 0.569 & 0.571 & 0.570 & 0:12:45 & [21, 15, 14, 2] \\ \hline
25.000 & 0.583 & 0.583 & 0.583 & 0:27:25 & [21, 15, 14, 2] \\ \hline
50.000 & 0.627 & 0.627 & 0.627 & 0:52:05 & [21, 15, 14, 2] \\ \hline
75.000 & 0.663 & 0.663 & 0.663 & 1:16:24 & [21, 35, 30, 25, 20, 2] \\ \hline
100.000 & 0.693 & 0.693 & 0.693 & 1:41:06 & [21, 25, 20, 2] \\ \hline
200.000 & 0.695 & 0.695 & 0.695 & 3:16:22 & [21, 40, 30, 20, 2] \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Multilayer perceptron.