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‘Classification’ Interview Questions

Top 25 Interview Questions on Classification with detailed Answers (All free)

Top 20 Interview Questions on Ensemble Learning with detailed Answers (All free)

What is Classification?

What is Multi-class Classification?

What are some of the common algorithms used for classification?

What is Logistic Regression?

What is Bagging? How do you perform bagging and what are its advantages?

What is a Decision Tree? Explain the concept and working of a Decision tree model

Explain the concept and working of the Random Forest model

What is Gradient Boosting (GBM)? Describe how does the Gradient Boosting algorithm work

How would you evaluate a classification model?

What are the major assumptions of logistic regression?

What are the advantages and disadvantages of Random Forest?

What are the advantages and disadvantages of a GBM model?

GBM vs Random Forest: which algorithm should be used when?

What is the difference between Adaboost and Gradient boost?

Distinguish between a Weak learner and a Strong Learner

What are the best ways to safeguard against overfitting a GBM?

What does Gradient in Gradient Boosted Trees refer to?

How is Gradient Boosting different from Random Forest?

What are the key hyperparameters for a GBM model?

Explain the difference between Entropy, Gini, and Information Gain

What are the key hyperparameters for a Random Forest model?

What are the options for reporting feature importance from a decision-tree based model?

What is XGBoost? How does it improve upon standard GBM?

What are the advantages and disadvantages of logistic regression?

What are the advantages and disadvantages of Decision Tree model?

How does a decision tree create splits from continuous features?

How would you address an imbalanced classification problem?

How to determine threshold/decision rule for a classification model?

Why is Random Forest a non-linear model? Why does it result in non-linear decision boundaries?

What is the difference between Discriminative and Generative models?

What is the error / loss function in logistic regression?

What is the basic idea of Support Vector Machine (SVM) and Maximum Margin?

How does pruning a tree work?

How Does Naive Bayes Work?

What are some pros and cons of Discriminant Analysis?

How does hinge loss differ from logistic loss?

What are common choices to use for kernels in SVM?

What is the kernel trick in SVM?

How does discriminant analysis work at a high level?

What is the difference between Decision Trees, Bagging and Random Forest?

How would you evaluate a Classification model using ROC/AUC?

What problems would arise from using a regular linear regression to model a binary outcome?

Among the common classification algorithms, which all produce probability estimates in addition to class labels?

How are continuous features incorporated into Naive Bayes?

What happens if a category has a zero frequency within a class, and how is this issue commonly addressed (Naive Bayes)?

What are options to calibrate probabilities produced from the output of a classifier that does not produce natural probabilities?

What do you mean by calibration quality? How can calibration quality be detected from the output of an algorithm?

What differentiates Linear Discriminant Analysis (LDA) from Quadratic Discriminant Analysis (QDA)?

What is the difference between QDA and Gaussian Mixture Models (GMM)?

What is False Positive Rate (FPR)?

What is Specificity?

What is F1 Score?

What is Precision?

What is Recall?

What is Misclassification rate?

What is Accuracy?

What are some of the pros/cons of SVM?

What are the Pros/Cons of Naive Bayes?

Explain how SVM can be used in regression problems

Describe the hinge loss function used in SVM

What hyper-parameters are typically tuned in SVM?

How does SVM adjust for classes that cannot be linearly separated?

What is the equivalent of the overall F test in logistic regression?

Why are coefficients estimated through Maximum Likelihood (MLE) instead of Least Squares?

How are the coefficients in a logistic expression interpreted?

What is the relationship between the log odds ratio and probability?

Why are the log odds used in the link function instead of just the regular odds ratio?

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