I did a number of machine learning experiments to predict a binary classification. I measured precision, recall and accuracy.

I noticed that my precision is generally quite high, and recall and accuracy are always the same numbers.

I used the following definitions:

Precision = TP / (TP + FP)

Recall: TP / (TP + FN)

Accuracy: (TP + TN) / (P + N)

I have some difficulties to interpret accuracy and recall. What does it mean if these two number are always the same in my case?

I did a number of machine learning experiments to predict a binary classification. I measured precision, recall and accuracy.

I noticed that my precision is generally quite high, and recall and accuracy are always the same numbers.

I used the following definitions:

$\text{Precision} = \frac{TP}{(TP + FP)}$

$\text{Recall} = \frac{TP}{(TP + FN)}$

$\text{Accuracy} = \frac{(TP + TN)}{(P + N)}$

I have some difficulties to interpret accuracy and recall. What does it mean if these two number are always the same in my case?