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Computer Vision (15)
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Classification

Q. What is Image Classification? Guide to CNN models and Applications
Q. Top 25 Interview Questions on Classification with detailed Answers
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 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. 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 CART?
Q. Explain the difference between Entropy, Gini, and Information Gain
Q. Regression vs. Classification
Q. What is Classification?

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Explore Questions by Topics

Computer Vision (15)
Generative AI (4)
Reinforcement Learning (13)
Machine Learning Basics (18)
Deep Learning (78)
- DL Basics (16)
- DL Architectures (21)
  - Feedforward Network / MLP (3)
  - Sequence models (6)
  - Transformers (11)
- DL Training and Optimization (39)
Natural Language Processing (34)
- NLP Data Preparation (18)
Supervised Learning (115)
- Regression (41)
  - Linear Regression (26)
  - Generalized Linear Models (9)
  - Regularization (6)
- Classification (70)
  - Logistic Regression (10)
  - Support Vector Machine (9)
  - Ensemble Learning (24)
  - Other Classification Models (9)
  - Classification Evaluations (9)
Unsupervised Learning (64)
- Clustering (40)
  - Distance Measures (9)
  - K-Means Clustering (10)
  - Hierarchical Clustering (3)
  - Gaussian Mixture Models (5)
  - Clustering Evaluations (5)
- Dimensionality Reduction (12)
Statistics (35)
Data Preparation (35)
- Feature Engineering (30)
- Sampling Techniques (5)

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Other Questions in Classification

What is a Gaussian Mixture Model (GMM)?
What is Supervised Fine-Tuning?
What is the problem with using a generic list of stop words?
What is Underfitting?
T5 Architecture Explained & Encoder-Decoder Model Comparison
Rewards in Reinforcement Learning: What They Are & How to Design

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