Precision vs. Recall Can I make the following statement about a binary classification, please?

Precision 1: 0.10
Recall 1: 0.83

Statement: "We can expect 90% false alarms (1 - 0.10). But for the remaining 10%, we can be around 83% certain (Recall 1: 0.83), that we caught a label 1."

Thanks in advance!
I would phrase it like so:

"Of all records that were labelled 1 by the model, 10% were actually 1 (90% incorrect predictions). Of all records that were truly labelled 1 we predicted 83% correctly."

While this is out of context of your question, if support refers to the number of records then it would be beneficial to get a more balanced dataset. The reason your precision is so poor for label 1 is because there are many more "negatives" (0's) than "positives" (1) increasing the chance for false positives to occur, affecting your precision.

EDIT:
This on Cross Validated will help provide more explanation.