COMPUTER SCIENCE AND ENGINEERING
MACHINE LEARNING
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Question
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Detailed explanation-1: -Model Output Most of the classification models output a probability number for the dataset. E.g. – A classification model like Logistic Regression will output a probability number between 0 and 1 instead of the desired output of actual target variable like Yes/No, etc.
Detailed explanation-2: -It is used to assess a classifier’s performance, and the output is a probability value between 1 and 0. A successful binary classification model should have a log loss value that is close to 0.
Detailed explanation-3: -Binary classification-when there is only two classes to predict, usually 1 or 0 values.
Detailed explanation-4: -Binary classification is used to predict one of two possible outcomes. A two class problem (binary problem) has possibly only two outcomes: “yes or no” “success” or “failure”