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Top 42 Dimensionality Reduction Interview Questions
Entry Junior Mid Senior Expert
Dimensionality Reduction Theoretical Questions
Q1:
Related To: Curse of Dimensionality, Data Processing
Why is data more sparse in a high-dimensional space?
Related To: Curse of Dimensionality, Data Processing Junior
Q2:
Related To: ML Design Patterns, Optimisation
When would you use Grid Search vs Random Search for Hyperparameter Tuning?
Related To: ML Design Patterns, Optimisation Junior
Junior
Q4:
Related To: ML Design Patterns, Model Evaluation, Optimisation
What methods of Hyperparameters Tuning do you know?
Related To: ML Design Patterns, Model Evaluation, Optimisation Junior
Q5:
Related To: Unsupervised Learning, Curse of Dimensionality
What is the Curse of Dimensionality and how can Unsupervised Learning help with it?
Related To: Unsupervised Learning, Curse of Dimensionality Junior
Junior
Junior
Q8:
Related To: Curse of Dimensionality
How does the Curse of Dimensionality affect Machine Learning models?
Related To: Curse of Dimensionality Junior
Q9:
Related To: Feature Engineering
Explain One-Hot Encoding and Label Encoding. Does the dimensionality of the dataset increase or decrease after applying these techniques?
Related To: Feature Engineering Junior
Q10:
Related To: Curse of Dimensionality, K-Nearest Neighbors
Does kNN suffer from the Curse of Dimensionality and if it why?
Related To: Curse of Dimensionality, K-Nearest Neighbors
Mid Q11:
Related To: Curse of Dimensionality, Data Mining
How does High Dimensionality affect Distance-Based Mining Applications?
Related To: Curse of Dimensionality, Data Mining Mid Q12:
Related To: Curse of Dimensionality
How does the Curse of Dimensionality affect Privacy Preservation?
Related To: Curse of Dimensionality Mid Q13:
How many Dimensionality Reduction Techniques do you know?
Mid Q14:
Explain the Sparse Random Projection
Mid Q15:
Related To: Clustering, Unsupervised Learning
What is the difference and connection between Clustering and Dimension Reduction?
Related To: Clustering, Unsupervised Learning Mid Q16:
What is the Crowding Problem?
Mid Mid Mid Q19:
How does Random Projection reduce the dimensionality of a set of points?
Mid Q20:
What is Singular Value Decomposition?
Mid Q21:
Related To: PCA, Linear Algebra
Why is Centering and Scaling the data important before performing PCA?
Related To: PCA, Linear Algebra Mid Q22:
What are the two branches of Dimensionality Reduction?
Mid Q23:
How does an Isomap perform Dimensionality Reduction?
Mid Mid Q25:
Related To: Curse of Dimensionality
Why does the hyperparameter optimisation method GridSearch suffer from the Curse of Dimensionality?
Related To: Curse of Dimensionality Senior Q26:
Related To: Unsupervised Learning
Explain the Locally Linear Embedding algorithm for Dimensionality Reduction
Related To: Unsupervised Learning Senior Q27:
Related To: SVM, Curse of Dimensionality
Does linear SVMs suffer from the Curse of Dimensionality?
Related To: SVM, Curse of Dimensionality Senior Q28:
Related To: Curse of Dimensionality, Deep Learning
How does a Deep Neural Network escape/resist the Curse of Dimensionality?
Related To: Curse of Dimensionality, Deep Learning Senior Senior Q30:
Related To: Random Forest, Curse of Dimensionality
Does Random Forest suffer from the Curse of Dimensionality?
Related To: Random Forest, Curse of Dimensionality Senior Q31:
How does Isometric Mapping (Isomap) work?
Senior Q32:
What is Independent Component Analysis?
Senior Q33:
Related To: Curse of Dimensionality, K-Means Clustering
How does the Curse of Dimensionality affect k-Means Clustering?
Related To: Curse of Dimensionality, K-Means Clustering Senior Q34:
What is t-Distributed Stochastic Neighbour Embedding?
Senior Q35:
What are the rules for generating a random matrix when Gaussian Random Projection is used?
Senior Q36:
What is Kernel Principal Component Analysis?
Senior Senior Senior Senior Q40:
How does the Metric Multidimensional Scaling (MDS) algorithm reduce dimensionality?
Expert Q41:
Related To: Data Processing
How does Normalization reduce the Dimensionality of the Data if you project the data to a Unit Sphere?
Related To: Data Processing Expert Dimensionality Reduction Practical Challenges
Q1:
Related To: Pandas, Feature Engineering
If we have a date column in our dataset, then how will you perform Feature Engineering using Python?
Related To: Pandas, Feature Engineeringdate column in our dataset, then how will you perform Feature Engineering using Python?
Junior 
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