10000 Added Fall out , miss rate , specificity by srinathreddychitteti · Pull Request #21854 · scikit-learn/scikit-learn · GitHub
[go: up one dir, main page]
Skip to content

Added Fall out , miss rate , specificity #21854

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Closed
wants to merge 2 commits into from
Closed

Added Fall out , miss rate , specificity #21854

Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
1fb18d7
Added Fall out , miss rate , specificity
srinathreddychitteti Dec 2, 2021
52b1462
Added Fall out , miss rate , specificity
srinathreddychitteti Dec 2, 2021
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
63 changes: 63 additions & 0 deletions sklearn/metrics/_classification.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -2688,3 +2688,66 @@ def brier_score_loss(y_true, y_prob, *, sample_weight=None, pos_label=None):
raise
y_true = np.array(y_true == pos_label, int)
return np.average((y_true - y_prob) ** 2, weights=sample_weight)


def Fall_out(y_pred , y_true):
mean = (None , )
warn_for = "Fall Out"
zero_division = "warn"
y_pred , y_true = column_or_1d(y_pred) , column_or_1d(y_true)
assert_all_finite(y_pred)
assert_all_finite(y_true)
TN , FP , FN , TP = confusion_matrix(y_true, y_pred).ravel()
fall_out = _prf_divide(np.array([FP]),
np.array([FP + TN]),
"Fall Out",
"predicted",
mean,
warn_for,
zero_division
)
result = fall_out[0]
return result



def Miss_rate(y_pred , y_true):
mean = (None , )
warn_for = "Miss Rate"
zero_division = "warn"
y_pred , y_true = column_or_1d(y_pred) , column_or_1d(y_true)
assert_all_finite(y_pred)
assert_all_finite(y_true)
TN , FP , FN , TP = confusion_matrix(y_true, y_pred).ravel()
miss_rate = _prf_divide(np.array([FN]),
np.array([FN + TP]),
"Miss Rate",
"predicted",
mean,
warn_for,
zero_division
)
result = miss_rate[0]
return result



def Specificity(y_pred , y_true):
mean = (None , )
warn_for = "Specificity"
zero_division = "warn"
y_pred , y_true = column_or_1d(y_pred) , column_or_1d(y_true)
assert_all_finite(y_pred)
assert_all_finite(y_true)
TN , FP , FN , TP = confusion_matrix(y_true, y_pred).ravel()
specificity = _prf_divide(np.array([TN]),
np.array([TN + FP]),
"Specificity",
"predicted",
mean,
warn_for,
zero_division
)
result = specificity[0]
return result

0