Interpreting high precision and very low recall score I was training model on a very imbalanced dataset with 80:20 ratio of two classes. The dataset has thousands of rows and I trained the model using 

DeccisionTreeClassifier(class_weight='balanced')

The precision and recall I get on the test set were very strange

Test set precision : 0.987767
Test set recall : 0.01432


I'm unable to interpret the results. What does this tell me about my classifier?
All problems due to "a very imbalanced dataset". Please look at the definition of recall and precision. Based on your score I could say that you a very small set of values labeled as positive, which are classified correctly($precision=\frac{TP}{TP+FP}$). But you have a very big set of values labeled as negative, which have influence on $recall=\frac{TP}{TP+FN}$, in that way that $TP$ stays the same like in precision, but have you a lot of $FN$ values which leads to small value of recall.

At least you should resample of the dataset to make better results.