Machine Learning Interview Questions

Q. What is Probabilistic (Fuzzy) Clustering?
Q. What is Exclusive Clustering?
Q. What are the most common categories of clustering?
Q. What is a closed form solution, and what are the advantages of a problem having such a solution? Which algorithms have a closed form solution?
Q. How does gradient descent differ from coordinate descent?
Q. What are the different types of Gradient Descent?
Q. What is Gradient Descent?
Q. What is the Curse of Dimensionality?
Q. What is Data Sparsity?
Q. What is Data Leakage?
Q. Distinguish between Structured and Unstructured Data
Q. What is the difference between Supervised and Unsupervised Learning
Q. What are the subtypes of Cross Validation?
Q. How does Cross Validation Work?
Q. How are model hyper-parameters generally selected?
Q. What is the purpose of feature selection, and what are some common approaches?
Q. Among the common machine learning algorithms, which require feature scaling, and which do not?
Q. Discuss Discretization in the context of feature engineering
Q. Discuss Timestamp Date Extraction in the context of feature engineering
Q. Discuss text feature extraction in the context of feature engineering
Q. Discuss Ordinal encoding in the context of feature engineering
Q. Discuss Dummy encoding in the context of feature engineering
Q. What are some of the most common feature engineering techniques?
Q. What is Feature Engineering?
Q. How can overfitting be mitigated in a machine learning model?
Q. How can underfitting be mitigated?
Q. How does a learning curve give insight into whether the model is under- or over-fitting?
Q. What is the Bias/Variance Tradeoff?
Q. What is Overfitting?
Q. What is Underfitting?
Q. How does Machine Learning differ from Classical Statistics and Deep Learning?
Q. What is Machine Learning?
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 Tweedie Regression?
Q. What is Beta regression?
Q. What is Gamma Regression?
Q. Briefly discuss other models that fall within the scope of GLM.
Q. What about cases where a significant number of observations have a count of 0 (in the context of Poisson Regression)?
Q. What is overdispersion in Poisson Regression, and what are alternate specifications for when it is present?
Q. What is the cost function used in Poisson Regression?
Q. How does GLM adjust to the case of count data?
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 is a Generalized Linear Model (GLM)?
Q. What is non-negative least squares, and when is it used?
Q. What are potential problems encountered in Linear Regression?
Q. What is a high influence point?
Q. What is a high leverage point?
Q. What is an outlier?
Q. What is the difference between outliers, high leverage points, and high influence points?
Q. What is the difference between Regression and ANOVA?
Q. Why does multicollinearity result in poor estimates of coefficients in linear regression?
Q. Doesn’t polynomial regression violate the multicollinearity assumption for Linear Regression?
Q. What are some approaches for modeling non linear relationships?
Q. Differentiate between Linear Models and Non Linear Models
Q. What are the most common transformations when the target variable is not normally distributed?
Q. How can categorical predictors be incorporated in linear regression?
Q. Suppose there are a large number of predictors ‘p’. What is the best approach to find out if any of the p predictors are helpful in predicting the response ‘y’?
Q. What are some of the problems with stepwise selection approaches?
Q. What is Information Criteria (AIC, BIC)?

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Machine Learning Interview Questions

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