## 5 Results
In particular, the highest values of recall and F1-score were obtained with batch size set to 20,000 blocks training the models with batch number 7 (0.46 and 0.52, respectively), while the highest values of precision is obtained by training the models with batch number 9 (0.86), as shown in Figure 6(c). Decreasing the batch size down to 10,000 generates a linear variance in precision between 0.25 and 0.75, keeping the F1-score in range 0.25-0.50 (Figure 6(d)). Figure 8 provides another interesting picture of the Mining Pool behavior. These graphs represent confusion matrices where the values of shown F1-scores were not averaged but represent the actual values computed for each test run. Each cell-value represents the F1-score of the models trained with the i-th batch and tested with the j-th batch. In case of \(i=j\), the value was not computed setting the cell to \(-1\) (due to this, they are shown in black). Using heatmaps [48], it becomes clear that decreasing the batch size generates an improvement in terms of F1-score in the detection of Mining Pool behavioral patterns (Figure 7(a)-d). In the first experiment, there were 0 elements with F1-score above 0.70 (a threshold that we chose to indicate a good classification). In fact, the highest value was 0.38. In the second experiment, there was just one test with F1-score above the chosen threshold reaching the value of 0.71. In the third experiment, no values above the threshold were achieved; however, the highest value of 0.67 was reached in three different cases. In the fourth experiment (smallest batch size of 10,000), 14 tests presented values above the threshold reaching a maximum value of 0.88. Interestingly, Figure 7(c) (batch size 20,000) clearly shows how \"early\" models (trained on temporally earlier blocks) up to batch number 6 do not yield good classification performance in neither previous nor following batches. Models trained on batches 6-8, though, achieved good performance in the first batches highlighting that generally it was more difficult to \"predict the future\" (earlier models perform poorly for future batches). Moreover, Mining Pool behavior in batch 7, for example, seems to represent a \"typical\" behavioral pattern that is recurrent over time across several batches. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Class**} & \multicolumn{2}{c}{**Batch**} & \multicolumn{2}{c}{**Batch**} & \multicolumn{2}{c}{**Batch**} & \multicolumn{2}{c}{**Batch**} \\  & \multicolumn{2}{c}{**60,000**} & \multicolumn{2}{c}{**30,000**} & \multicolumn{2}{c}{**20,000**} & \multicolumn{2}{c}{**10,000**} \\ \cline{2-9}  & \begin{tabular}{c} **F1** \\ **Average** \\ \end{tabular} & \begin{tabular}{c} **std. dev.** \\ **Average** \\ \end{tabular} & \begin{tabular}{c} **F1** \\ **Average** \\ \end{tabular} & \begin{tabular}{c} **std. dev.** \\ **Average** \\ \end{tabular} & \
\begin{tabular}{c} **F1** \\ **Average** \\ \end{tabular} &
\begin{tabular}{c} **std. dev.** \\ **Average** \\ \end{tabular} \\ \hline _Exchange_ & 0.68 & 0.047 & 0.71 & 0.039 & 0.70 & 0.040 & 0.72 & 0.050 \\ _Gambling_ & 0.34 & 0.052 & 0.34 & 0.071 & 0.32 & 0.070 & 0.39 & 0.084 \\ _Market_ & 0.07 & 0.050 & 0.06 & 0.054 & 0.08 & 0.099 & 0.08 & 0.112 \\ _Mining Pool_ & 0.34 & 0.038 & 0.24 & 0.175 & 0.28 & 0.138 & 0.29 & 0.218 \\ _Mixer_ & 0.16 & 0.143 & 0.08 & 0.083 & 0.10 & 0.128 & 0.07 & 0.099 \\ _eWallet_ & 0.10 & 0.037 & 0.15 & 0.126 & 0.14 & 0.098 & 0.13 & 0.102 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: F1-score average and standard deviation average computed with data from each experiment. Figure 8: Mining Pool F1-score of each test in the four presented experiments. Figure 7: Radar graph with precision, recall, F1-score, and number of samples (normalized) for the Mining Pool class.