precision recall curve is like stairs
A precision-recall curve (as well as a ROC curve) is made from the different thresholds which can be used to separate predictions between positive and negative instances. These thresholds naturally depend on the possible values predicted by the classifier.
For example, if the classifier predicts only 3 different values $a&lt;b&lt;c$, then the threshold $t$ can only be in these positions:

$t&lt;a$
$a&lt;t&lt;b$
$b&lt;t&lt;c$
$c&lt;t$

Each of these positions will correspond to one pair of values for precision and recall, so exactly one point in the curve. Therefore the shape of the curve (how smooth it is) depends on how many different values the classifier predicts.
Thus a stairs-shaped curve shows that the classifier predicts only a few different values. There can be various reasons for that, but since you mention ensemble learning I guess that the final predicted value is based on the proportion of individual learners predicting the positive class, right? If yes that would explain it: if there are $N$ individual learners then the final predicted value can only be $0,1/N,2/N,...,N$. This means a maximum of $N+1$ different values and therefore a maximum of $N+1$ points on the curve.