diff --git a/cyclops/evaluate/metrics/experimental/functional/specificity.py b/cyclops/evaluate/metrics/experimental/functional/specificity.py index 4b5de6074..6fbd3e4b3 100644 --- a/cyclops/evaluate/metrics/experimental/functional/specificity.py +++ b/cyclops/evaluate/metrics/experimental/functional/specificity.py @@ -315,7 +315,7 @@ def multilabel_specificity( average: Optional[Literal["micro", "macro", "weighted", "none"]] = "macro", ignore_index: Optional[int] = None, ) -> Array: - """Measure the proportion of positive predictions that are true positive. + """Measure how well a classifier identifies negative samples. Parameters ---------- diff --git a/cyclops/evaluate/metrics/experimental/negative_predictive_value.py b/cyclops/evaluate/metrics/experimental/negative_predictive_value.py index 4936674d2..7a5f1e5ee 100644 --- a/cyclops/evaluate/metrics/experimental/negative_predictive_value.py +++ b/cyclops/evaluate/metrics/experimental/negative_predictive_value.py @@ -12,7 +12,7 @@ class BinaryNPV(_AbstractBinaryStatScores, registry_key="binary_npv"): - """The proportion of predictive negatives that are true negatives. + """The proportion of negative predictions that are true negatives. Parameters ---------- @@ -54,7 +54,7 @@ class MulticlassNPV( _AbstractMulticlassStatScores, registry_key="multiclass_npv", ): - """The proportion of predictive negatives that are true negatives. + """The proportion of negative predictions that are true negatives. Parameters ---------- @@ -122,7 +122,7 @@ class MultilabelNPV( _AbstractMultilabelStatScores, registry_key="multilabel_npv", ): - """The proportion of actual negatives that are correctly identified. + """The proportion of negative predictions that are true negatives. Parameters ---------- diff --git a/cyclops/evaluate/metrics/f_beta.py b/cyclops/evaluate/metrics/f_beta.py index 59e6659de..575bacb7c 100644 --- a/cyclops/evaluate/metrics/f_beta.py +++ b/cyclops/evaluate/metrics/f_beta.py @@ -178,7 +178,7 @@ class MultilabelFbetaScore(MultilabelStatScores, registry_key="multilabel_fbeta_ num_labels : int Number of labels for the task. threshold : float, default=0.5 - Threshold for deciding the positive class if predicitions are logits + Threshold for deciding the positive class if predictions are logits or probability scores. Logits will be converted to probabilities using the sigmoid function. top_k : int, optional diff --git a/tests/cyclops/evaluate/metrics/experimental/test_specificity.py b/tests/cyclops/evaluate/metrics/experimental/test_specificity.py index b905a15d2..035edbada 100644 --- a/tests/cyclops/evaluate/metrics/experimental/test_specificity.py +++ b/tests/cyclops/evaluate/metrics/experimental/test_specificity.py @@ -1,4 +1,4 @@ -"""Test specificity recall metrics.""" +"""Test specificity.""" from functools import partial from typing import Literal, Optional