Skip to main content
Skip to footer
Home
Interview Questions
Machine Learning Basics
Deep Learning
Supervised Learning
Unsupervised Learning
Natural Language Processing
Statistics
Data Preparation
Jobs
Home
Interview Questions
Machine Learning Basics
Deep Learning
Supervised Learning
Unsupervised Learning
Natural Language Processing
Statistics
Data Preparation
Jobs
Login
Sign Up
Explore Questions by Topics
Computer Vision
(1)
–
Data Preparation
(35)
Feature Engineering
(30)
Sampling Techniques
(5)
–
Deep Learning
(52)
–
DL Architectures
(17)
Feedforward Network / MLP
(2)
Sequence models
(6)
Transformers
(9)
DL Basics
(16)
DL Training and Optimization
(17)
Generative AI
(2)
Machine Learning Basics
(18)
–
Natural Language Processing
(27)
NLP Data Preparation
(18)
Statistics
(34)
–
Supervised Learning
(115)
–
Classification
(70)
Classification Evaluations
(9)
Ensemble Learning
(24)
Logistic Regression
(10)
Other Classification Models
(9)
Support Vector Machine
(9)
–
Regression
(41)
Generalized Linear Models
(9)
Linear Regression
(26)
Regularization
(6)
–
Unsupervised Learning
(55)
–
Clustering
(37)
Clustering Evaluations
(6)
Distance Measures
(9)
Gaussian Mixture Models
(5)
Hierarchical Clustering
(3)
K-Means Clustering
(9)
Dimensionality Reduction
(9)
Logistic Regression
Q.
What is Logistic Regression?
Q.
What is the error / loss function in logistic regression?
Q.
What are the advantages and disadvantages of logistic regression?
Q.
What is the equivalent of the overall F test in logistic regression?
Q.
Why are coefficients estimated through Maximum Likelihood (MLE) instead of Least Squares?
Q.
How are the coefficients in a logistic expression interpreted?
Q.
What is the relationship between the log odds ratio and probability?
Q.
Why are the log odds used in the link function instead of just the regular odds ratio?
Q.
What problems would arise from using a regular linear regression to model a binary outcome?
Q.
What are the assumptions of logistic regression?
Partner Ad
Explore Questions by Topics
Computer Vision
(1)
–
Data Preparation
(35)
Feature Engineering
(30)
Sampling Techniques
(5)
–
Deep Learning
(52)
–
DL Architectures
(17)
Feedforward Network / MLP
(2)
Sequence models
(6)
Transformers
(9)
DL Basics
(16)
DL Training and Optimization
(17)
Generative AI
(2)
Machine Learning Basics
(18)
–
Natural Language Processing
(27)
NLP Data Preparation
(18)
Statistics
(34)
–
Supervised Learning
(115)
–
Classification
(70)
Classification Evaluations
(9)
Ensemble Learning
(24)
Logistic Regression
(10)
Other Classification Models
(9)
Support Vector Machine
(9)
–
Regression
(41)
Generalized Linear Models
(9)
Linear Regression
(26)
Regularization
(6)
–
Unsupervised Learning
(55)
–
Clustering
(37)
Clustering Evaluations
(6)
Distance Measures
(9)
Gaussian Mixture Models
(5)
Hierarchical Clustering
(3)
K-Means Clustering
(9)
Dimensionality Reduction
(9)
Search
Join us on:
Machine Learning Interview Preparation Group
@OfficialAIML
Find out all the ways that you can
Contribute
Other Questions in Logistic Regression
Why are coefficients estimated through Maximum Likelihood (MLE) instead of Least Squares?
How does SVM adjust for classes that cannot be linearly separated?
Top 50 Supervised Learning Interview Questions with detailed Answers (All free)
Describe the hinge loss function used in SVM
What does L2 regularization (Ridge) mean?
What are some pros and cons of Discriminant Analysis?