Suppose that you have trained a logistic regression classifier, and it outputs on a new example x a prediction hÎ¸(x) = 0.7. This means (check all that apply):
Our estimate for P(y=0|x;Î¸) is 0.7.
Our estimate for P(y=1|x;Î¸) is 0.7.
Our estimate for P(y=0|x;Î¸) is 0.3.
Our estimate for P(y=1|x;Î¸) is 0.3.

Question

09-12-2022
Mathematics

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Suppose that you have trained a logistic regression classifier, and it outputs on a new example x a prediction hÎ¸(x) = 0.7. This means (check all that apply):
Our estimate for P(y=0|x;Î¸) is 0.7.
Our estimate for P(y=1|x;Î¸) is 0.7.
Our estimate for P(y=0|x;Î¸) is 0.3.
Our estimate for P(y=1|x;Î¸) is 0.3.

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abhinavjh321 abhinavjh321 · Answer 1 · 2022-12-18T11:32:50+01:00

The prediction hÎ¸(x) = 0.7 means that the probability of the example having a value of y=1 is 0.7 (or 70%). Our estimate for P(y=0|x;Î¸) is 0.3. and Our estimate for P(y=1|x;Î¸) is 0.7.

The prediction hÎ¸(x) = 0.7 means that the probability of the example having a value of y=1 is 0.7 (or 70%). This means that the probability of the example having a value of y=0 is 0.3 (or 30%). Therefore, our estimate for P(y=0|x;Î¸) is 0.3 and our estimate for P(y=1|x;Î¸) is 0.7.

The logistic regression classifier is a supervised learning algorithm used for binary classification. It is used to estimate the probability of an example belonging to one of two classes, typically 0 or 1. The output of the classifier, hÎ¸(x), is a value between 0 and 1, which is interpreted as the probability of the example belonging to class 1. In the given case, the output of the classifier is hÎ¸(x) = 0.7, which implies that the probability of the example belonging to class 1 is 0.7 (or 70%) and the probability of the example belonging to class 0 is 0.3 (or 30%). Thus, our estimate for P(y=0|x;Î¸) is 0.3 and our estimate for P(y=1|x;Î¸) is 0.7.

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