Machine Learning Interview Questions

Q. What are options to calibrate probabilities produced from the output of a classifier that does not produce natural probabilities?
Q. What happens if a category has a zero frequency within a class, and how is this issue commonly addressed (Naive Bayes)?
Q. What are the Pros/Cons of Naive Bayes?
Q. What hyper-parameters are typically tuned in SVM?
Q. What are some of the pros/cons of SVM?
Q. How does discriminant analysis work at a high level?
Q. What are some pros and cons of Discriminant Analysis?
Q. Discuss Ordinal encoding in the context of feature engineering
Q. Discuss Timestamp Date Extraction in the context of feature engineering
Q. Discuss Discretization in the context of feature engineering
Q. Among the common machine learning algorithms, which require feature scaling, and which do not?
Q. What are the subtypes of Cross Validation?
Q. What is Data Leakage?
Q. What is Data Sparsity?
Q. How does gradient descent differ from coordinate descent?
Q. What is Exclusive Clustering?
Q. What is Probabilistic (Fuzzy) Clustering?
Q. What is Model-based Clustering?
Q. What is Within Cluster Sum of Squares (WCSS)?
Q. What is Silhouette Score?
Q. What is Dunn Index?
Q. What is Rand Index?
Q. What is Adjusted Rand Index (ARI)?
Q. What is Mutual Information (MI)?
Q. What is Euclidean Distance?
Q. What is Mahalanobis Distance?
Q. What is Manhattan Distance?
Q. What is Minkowski Distance?
Q. What is Cosine Similarity?
Q. What is Jaccard Index / Distance?
Q. What is KL Divergence?
Q. How does K-Means Work?
Q. How can you choose the optimal value for ‘k’ in K-Means?
Q. What is the effect of minimizing the within-cluster sum of squares on the shapes of clusters produced in K-Means?
Q. How do outliers affect the clusters formed in K-Means?
Q. What are the Pros and Cons of K-Means Clustering?
Q. What are the two ways in which Hierarchical clustering can proceed?
Q. What are some of the possible linkage types to use in order to form successive clusters?
Q. How does imposing connectivity constraints help with Agglomerative clustering?
Q. What is a dendrogram, and how is it used in hierarchical clustering?
Q. What are some of the pros and cons of hierarchical clustering compared to K-Means?
Q. How is clustering affected by high-dimensional data, and how can the quality of clusters generated be improved in such cases?
Q. What is Bi-Clustering? What are possible use cases of it?
Q. What is Spectral co-clustering?
Q. What are some options for identifying the number of components in a GMM?
Q. How does the EM algorithm (in the context of GMM) compare to K-Means?
Q. Pros and Cons of Gaussian Mixture Models (GMM) Clustering
Q. What is Kernel PCA?
Q. What is Independent Component Analysis (ICA), and how is it distinguished from PCA?
Q. What is Factor Analysis, and how does it differ from PCA?
Q. How does T-distributed Stochastic Neighbor Embedding (T-SNE) work at a high level?
Q. How does T-SNE compare to PCA?
Q. What is the difference between a parameter and a statistic?
Q. What is a probability function, and what properties must it satisfy?
Q. What does it mean for two events to be mutually exclusive?
Q. What does it mean for two events to be independent?
Q. What is conditional probability?
Q. What is Bayes’ Rule?
Q. What is a random variable?
Q. What is Kolmogorov–Smirnov statistic?
Q. What does it mean if observations are iid, and why is this a desirable property?
Q. What is the difference between probability and non-probability sampling, and what are some example methodologies for each?
Q. What is Simple Random Sampling?
Q. What is Stratified Sampling?
Q. What is Cluster Sampling?
Q. What is a Z Score?
Q. What is the Empirical Rule?
Q. What is Chebyshev’s Theorem and its implications?
Q. What is the difference between covariance and correlation?
Q. What is the relationship between independence and correlation?
Q. What are some desirable properties of estimators?
Q. What is the Central Limit Theorem (CLT), and what are its implications for statistical inference?
Q. What is a Confidence Interval?
Q. What is the difference between Mean, Median and Mode?
Q. How to choose between mean and median to summarize data?
Q. What is Skewness and Kurtosis?
Q. What is an Outlier?
Q. What are some options for dealing with outliers?
Q. What are some automatic outlier detection mechanisms?
Q. What is Isolation Forest?
Q. What is Local Outlier Factor?
Q. How does Bayesian Statistics differ from the Frequentist paradigm?
Q. What are the main components of a Bayesian Model?
Q. What is bootstrapping, and why is it a useful technique?
Q. How does Deep Learning methods compare with traditional Machine Learning methods?
Q. What is Deep Learning? Discuss the key characteristics, working and applications of Deep Learning
Q. What are the advantages and disadvantages of Deep Learning?
Q. What is a Perceptron? What is the role of bias in a perceptron (or neuron)?
Q. What is a Multilayer Perceptron (MLP) or a Feedforward Neural Network (FNN)?
Q. Explain the basic architecture of a Neural Network, model training and key hyper-parameters
Q. What is the difference between Deep and Shallow networks?
Q. What is an activation function, and what are some of the most common choices for activation functions?
Q. What is Sigmoid (logistic) activation function?
Q. Discuss TanH activation function
Q. What is Rectified Linear Unit (ReLU) activation function? Discuss its advantages and disadvantages
Q. Discuss Softmax activation function
Q. What are some guidelines for choosing activation functions?
Q. How are Regression and Classification performed using multilayer perceptrons (MLP)?
Q. What is Backpropagation?
Q. What are some options for making Backpropagation more efficient?

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Machine Learning Interview Questions

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