How to interpret the rec curve for a regression task?
The title of the graph says &quot;REC curve for various models&quot; but this curve is for a single model. It shows as Y the percentage of &quot;correct&quot; predictions by the model depending on what &quot;correct&quot; means, which is given as X: for example if correct means an error less than 10 ha (on X axis), then the model is correct around 55% of the time (on Y axis). In other words the graph splits the instances between correct/incorrect ones based on whether the error value (difference between true and predicted value) is lower than a threshold X.
In particular this graph shows that:

this model predicts very few values with an error less than 8
this model very often (around 50%) predicts values which are 8-9 ha off the true value.
there are couple more values that the model predicts with an error around 15.
more than 40% of the predicted values are not visible on this graph, which means that their error is higher than 20.

I'd suggest plotting the true vs. predicted value, it's easier to interpret (although it can be harder to see where are the large groups of instances).