Skip to main content Skip to footer

Home
Interview Questions
Jobs

Home
Interview Questions
Jobs

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)

Classification

Q. Top 20 Interview Questions on Ensemble Learning with detailed Answers (All free)
Q. Top 25 Interview Questions on Classification with detailed Answers (All free)
Q. What is Logistic Regression?
Q. What is the difference between Discriminative and Generative models?
Q. What are some pros and cons of Discriminant Analysis?
Q. What is the difference between QDA and Gaussian Mixture Models (GMM)?
Q. What differentiates Linear Discriminant Analysis (LDA) from Quadratic Discriminant Analysis (QDA)?
Q. How does discriminant analysis work at a high level?
Q. What are some of the pros/cons of SVM?
Q. Explain how SVM can be used in regression problems
Q. How does hinge loss differ from logistic loss?
Q. Describe the hinge loss function used in SVM
Q. What hyper-parameters are typically tuned in SVM?
Q. What are common choices to use for kernels in SVM?
Q. What is the kernel trick in SVM?
Q. How does SVM adjust for classes that cannot be linearly separated?
Q. What is the basic idea of Support Vector Machine (SVM) and Maximum Margin?
Q. What are the Pros/Cons of Naive Bayes?
Q. How are continuous features incorporated into Naive Bayes?
Q. What happens if a category has a zero frequency within a class, and how is this issue commonly addressed (Naive Bayes)?
Q. How Does Naive Bayes Work?
Q. What are options to calibrate probabilities produced from the output of a classifier that does not produce natural probabilities?
Q. What do you mean by calibration quality? How can calibration quality be detected from the output of an algorithm?
Q. Understanding Probability Outputs in Classification Algorithms
Q. What are some of the common algorithms used for classification?
Q. What is Multi-class Classification?
Q. How to determine threshold/decision rule for a classification model?
Q. How would you address an imbalanced classification problem?
Q. How would you evaluate a Classification model using ROC/AUC?
Q. What is False Positive Rate (FPR)?
Q. What is Specificity?
Q. What is F1 Score?
Q. What is Precision?
Q. What is Recall?
Q. What is Misclassification rate?
Q. What is Accuracy?
Q. How would you evaluate a classification model?
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?
Q. What does Gradient in Gradient Boosted Trees refer to?
Q. What is XGBoost? How does it improve upon standard GBM?
Q. What is the difference between Adaboost and Gradient boost?
Q. Distinguish between a Weak learner and a Strong Learner
Q. What are the options for reporting feature importance from a decision-tree based model?
Q. What are the best ways to safeguard against overfitting a GBM?
Q. GBM vs Random Forest: which algorithm should be used when?
Q. How is Gradient Boosting different from Random Forest?
Q. What are the advantages and disadvantages of a GBM model?
Q. What are the key hyperparameters for a GBM model?
Q. What is Gradient Boosting (GBM)? Describe how does the Gradient Boosting algorithm work
Q. What is the difference between Decision Trees, Bagging and Random Forest?
Q. Why is Random Forest a non-linear model? Why does it result in non-linear decision boundaries?
Q. What are the advantages and disadvantages of Random Forest?
Q. What are the key hyperparameters for a Random Forest model?
Q. Explain the concept and working of the Random Forest model
Q. What is Bagging? How do you perform bagging and what are its advantages?
Q. What are the advantages and disadvantages of Decision Tree model?
Q. What is CART?
Q. How does pruning a tree work?
Q. How does a decision tree create splits from continuous features?
Q. Explain the difference between Entropy, Gini, and Information Gain
Q. What is a Decision Tree? Explain the concept and working of a Decision tree model
Q. What is Classification?

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

Other Questions in Classification

What is Within Cluster Sum of Squares (WCSS)?
What are some approaches for modeling non linear relationships?
What is Kernel PCA?
What problems would arise from using a regular linear regression to model a binary outcome?
What is the problem with using a generic list of stop words?
What is Classification?

© 2025 AIML.COM | ♥ Sunnyvale, California