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ln(p1−p)=β0+β1X The equation of line in the above equation denotes that the log of odds is linearly related to the predictor variables. Why is log of odds linearly related to the predictor variables, …

Can someone provide me an example/link of how log odds is calculated in logistic regression (with multiple independent variables)? All the examples I've come across explain log odds calculation with a …

Log converts values from multiplicative scale to additive scale. What is the advantage of an additive model in logistic regression over a multiplicative model for which we use log?

In Logistic regression, the final values we achieve are associated with Probability. Then why do we need Logit/Log of odds? We can directly use probability. Is Logit used to get the equation of a best …

Equation of log odds: Example: Log odds of getting a heart disease--> 0.8=2.5(Hypertension)+0.3(Gender)+0.06(Age)+15 How is this equation interpreted?

For example: The probabilistic approach of logistic regression involves the MLE (maximum likelihood estimation) maximizing the likelihood function, or in other words, finding the best parameters for t …

When mapping probabilities obtained in logistic regression to 0s & 1s using the sigmoid function, we use a threshold value of 0.5. If the predicted probability lies above 0.5, then it gets mapped to 1 …

Sigmoid function predicts the probability value which is between 0 & 1. What is the formula in logistic regression that maps the predicted probabilities to either 1 or 0?

Here's an excerpt from a paper explaining Logistic Regression. What does 'Closed Form' mean in this context? Please explain in simple terms. The definitions online are confusing. Gradient of Log Like …

Here's a log odds equation --> 0.8=2.5(Hypertension)+0.3(Gender)+0.06(Age)+15 Please let me know if my interpretation of it is right --> My interpretation: With one unit change in 'Hypertension', log …

What are the similarities & dissimilarities between MLE (used to find the best parameters in logistic regression) & Gradient Descent?

In Logistic Regression, MLE is used to develop a mathematical function to estimate the model parameters, optimization techniques like Gradient Descent are used to solve this function. Can somebody ple …

Interpretation of log odds with a single predictor variable --> 0.8=2.5(Hypertension) The odds of a person with hypertension getting heart disease is 0.8. Log odds with multiple predictor variables -- …

Odds is the chance of an event occurring against the event not occurring. Likelihood is the probability of a set of parameters being supported by the data in hand. In logistic regression, we use log o …

$\log(\text{odds}) = \text{logit}(P)=ln \big({{P}\over{1-P}}\big)$ $ln\big({{P}\over{1-P}}\big)=\beta_0+\beta_1x$ Consider this example: $0.7=\beta_o+\beta_1(x)+\beta_2(y)+\beta_3(z)$ How can this exp …