Table of Contents

- 1 Why do we model log odds in logistic regression?
- 2 Why do we use log of odds?
- 3 Why is the loss function different in linear regression and logistic regression?
- 4 What are the relationships between the coefficient in the logistic regression and the odds ratio?
- 5 What is the difference between odds and chances?
- 6 How do you interpret odds ratio in logistic regression?
- 7 What is the difference between logistics and logarithms?
- 8 When should you consider using logistic regression?
- 9 What are the assumptions of logistic regression?

## Why do we model log odds in logistic regression?

Log odds play an important role in logistic regression as it converts the LR model from probability based to a likelihood based model. Both probability and log odds have their own set of properties, however log odds makes interpreting the output easier.

### Why do we use log of odds?

Log of Odds = log (p/(1-P)) Fig 3: Logit Function heads to infinity as p approaches 1 and towards negative infinity as it approaches 0. That is why the log odds are used to avoid modeling a variable with a restricted range such as probability.

#### Why is the loss function different in linear regression and logistic regression?

The purpose of Linear Regression is to find the best-fitted line while Logistic regression is one step ahead and fitting the line values to the sigmoid curve. The method for calculating loss function in linear regression is the mean squared error whereas for logistic regression it is maximum likelihood estimation.

**Is logistic regression the same as logarithmic?**

The biggest difference would be that logistic regression assumes the response is distributed as a binomial and log-linear regression assumes the response is distributed as Poisson.

**What is the difference between odds and odds ratio?**

Odds are the probability of an event occurring divided by the probability of the event not occurring. An odds ratio is the odds of the event in one group, for example, those exposed to a drug, divided by the odds in another group not exposed.

## What are the relationships between the coefficient in the logistic regression and the odds ratio?

More precisely, if b is your regression coefficient, exp(b) is the odds ratio corresponding to a one unit change in your variable. So, to get back to the adjusted odds, you need to know what are the internal coding convention for your factor levels. Usually, for a binary variable it is 0/1 or 1/2.

### What is the difference between odds and chances?

Note that there is a subtle difference between chances and odds: ‘odds’ represent a ratio of probability, such as 3:1 (three to one; out of 4 times it will happen 3 times and one time not), while ‘chances’ represent a direct probability, such as 75\%, which equals 3:1 odds.

#### How do you interpret odds ratio in logistic regression?

To conclude, the important thing to remember about the odds ratio is that an odds ratio greater than 1 is a positive association (i.e., higher number for the predictor means group 1 in the outcome), and an odds ratio less than 1 is negative association (i.e., higher number for the predictor means group 0 in the outcome …

**Is log loss a better error metric for logistic regression?**

Problem with the linear line: That is where `Logistic Regression` comes in.

**Why is regularization useful in logistic regression?**

Regularization can be used to avoid overfitting. In other words: regularization can be used to train models that generalize better on unseen data, by preventing the algorithm from overfitting the training dataset. …

## What is the difference between logistics and logarithms?

As adjectives the difference between logistic and logarithmic. is that logistic is (operations) relating to logistics while logarithmic is (mathematics) of, or relating to logarithms.

### When should you consider using logistic regression?

First, you should consider logistic regression any time you have a binary target variable. That’s what this algorithm is uniquely built for, as we saw in the last chapter. that comes with logistic…

#### What are the assumptions of logistic regression?

Assumptions of Logistic Regression. This means that the independent variables should not be too highly correlated with each other. Fourth, logistic regression assumes linearity of independent variables and log odds. although this analysis does not require the dependent and independent variables to be related linearly,…

**Why is logistic regression considered a linear model?**

The short answer is: Logistic regression is considered a generalized linear model because the outcome always depends on the sum of the inputs and parameters. Or in other words, the output cannot depend on the product (or quotient, etc.) “A statistician calls a model “linear” if the mean of the response is a linear function of the parameter, and this is clearly violated for logistic regression.

**What does logistic regression Tell Me?**

Purpose and examples of logistic regression. Logistic regression is one of the most commonly used machine learning algorithms for binary classification problems,which are problems with two class values,including