in 75% of the tests, whereas MSCN is more robust in the margins, resulting in a better mean.

![Estimation errors on the cnt\_test1 workload. In all the similar plots presented in this paper, the box boundaries are at the 25th/75th percentiles and the horizontal lines mark the 5th/95th percentiles. Hence, 50% of the tests results are located within the box boundaries, and 90% are located between the horizontal lines. The orange horizontal line mark the 50th percentile. []{data-label="fig:cnt_test1"}](cnt1.PNG){width="\linewidth"}

Generalizing to Additional Joins {#Generalizing to More Joins}
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In this section we examine how the CRN model generalizes to queries with a higher number of joins without having seen such queries during training. To do so, we use the crd\_test2 workload which includes queries with zero to *five* joins. Recall that we trained both the CRN model and the MSCN model only with query pairs that have between zero and two joins. Examining the results, described in Table \[table:cnt\_test2\] and Figure \[fig:cnt\_test2\], the CRN model is noticeably more robust in generalizing to queries with additional joins. The mean q-error of the CRN model is smaller by a factor of almost 8 than the mean q-errors of the other models.

![Estimation errors 