Answer:
The logistic loss function, also known as the logistic regression loss or cross-entropy loss, is commonly used in binary classification problems. It measures the performance of a classification model whose output is a probability value between 0 and 1. The logistic loss function is given by:
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(
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,
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^
)
=
−
(
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log
(
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^
)
+
(
1
−
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)
log
(
1
−
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^
)
)
L(y,
y
^
)=−(ylog(
y
^
)+(1−y)log(1−
y
^
))
Where:
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y is the true label (0 or 1),
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^
y
^
is the predicted probability that
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=
1
y=1.
In the provided options, the logistic loss function is represented by the mathematical expression:
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(
�
,
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^
)
=
−
(
�
log
(
�
^
)
+
(
1
−
�
)
log
(
1
−
�
^
)
)
L(y,
y
^
)=−(ylog(
y
^
)+(1−y)log(1−
y
^
))
Explanation:
