To calculate precision on a theoretical frequency, you need to have a set of predicted values and corresponding actual values. Precision is a metric used in classification tasks to measure the accuracy of positive predictions.

The formula to calculate precision is:

code Precision = True Positives / (True Positives + False Positives)

Here, True Positives refer to the number of correctly predicted positive values, and False Positives refer to the number of incorrectly predicted positive values.

If you have a set of predicted values predictions and corresponding actual values you can calculate precision in Python using the scikit-learn library as follows:

Use python code from sklearn.metrics import precision_score precision = precision_score(actual_values, predictions)

Make sure that the predicted values and actual values are in the correct format. For example, if you have binary classification (0 and 1), both predictions and actual_values should be arrays/lists of 0s and 1s.

If you are working with a different programming language or framework, the syntax may vary, but the underlying concept of calculating precision remains the same.