Final answer:
Lowering the predict_proba threshold in a classifier model increases recall by identifying more positive cases but reduces precision by causing more false positive predictions.
Step-by-step explanation:
Adjusting the predict_proba threshold in classification models affects the balance between recall and precision. By lowering the threshold, we effectively make the classifier more sensitive to predicting positives. This typically increases the number of true positives as well as false positives, thereby increasing recall (also known as sensitivity or true positive rate), since a greater proportion of actual positive cases are correctly identified. However, this comes at the expense of precision, which measures the proportion of true positive predictions in relation to all positive predictions (both true positives and false positives). Lower precision indicates that while we might capture more of the relevant instances, we also make more incorrect positive predictions.
For instance, in a medical diagnosis scenario, lowering the threshold would result in more patients being identified as having a disease, which is beneficial if missing a diagnosis has severe consequences. On the other hand, this means that more healthy patients might be incorrectly diagnosed as sick, leading to unnecessary treatments or anxiety.