Odds and Log-odds
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Machine Learning
Linear Methods For Classification
Interview questions on Linear Methods For Classification.
5 questions
All subtopicsGeneral ConceptsOdds and Log-oddsThe SigmoidTruly Understanding Logistic RegressionThe Logit Function and Entropy
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Odds and Log-odds
Q2. Draw a graph of odds to probabilities, mapping the entire range of probabilities to their respective odds.
Odds and Log-odds
Q3. The logistic regression model is a subset of a broader range of machine learning models known as generalized linear models (GLMs), which also include analysis of variance (ANOVA), vanilla linear regression, etc. There are three components to a GLM; identify these three components for binary logistic regression.
Odds and Log-odds
Q4. Let us consider the logit transformation, i.e., log-odds. Assume a scenario in which the logit forms the linear decision boundary, for a given vector of systematic components X and predictor variables θ. Write the mathematical expression for the hyperplane that describes the decision boundary.$$
\log{\frac{Pr(Y = 1 | X)}{Pr(Y = 0|X)}} = \theta_0 + \theta^TX
Odds and Log-odds