COMPUTER SCIENCE AND ENGINEERING
MACHINE LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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Sigmoid
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polynomial
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square
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rbf
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Detailed explanation-1: -Logistic regression uses a logistic function called a sigmoid function to map predictions and their probabilities. The sigmoid function refers to an S-shaped curve that converts any real value to a range between 0 and 1.
Detailed explanation-2: -Logit function estimates probabilities between 0 and 1, and hence logistic regression is a non-linear transformation that looks like S-function shown below.
Detailed explanation-3: -What is the Sigmoid Function? In order to map predicted values to probabilities, we use the Sigmoid function. The function maps any real value into another value between 0 and 1. In machine learning, we use sigmoid to map predictions to probabilities.
Detailed explanation-4: -Conversion rule To convert a logit ( glm output) to probability, follow these 3 steps: Take glm output coefficient (logit) compute e-function on the logit using exp() “de-logarithimize” (you’ll get odds then) convert odds to probability using this formula prob = odds / (1 + odds) .
Detailed explanation-5: -Such a logistic model is called a log-odds model. Hence, in statistics, Logistic Regression is sometimes called the logistic model or logit model. It is used for predicting the probability of the occurrence of a specific event by fitting data to a logit Logistic Function curve.