awesome_machine_learning_interview_question
github.com/rohan-paul/awesome_machine_learning_interview_question ↗WORLD'S MOST COMPREHENSIVE RESOURCE FOR MACHINE LEARNING INTERVIEW QUESTIONS
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Anomaly_DetectionBiasVarianceBig-O_NotationClassificationK-Nearest_NeighborsAutoencodersClusteringCost_FunctionData_ProcessingFeature_EngineeringGeneral
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General
- Addressing Correlated Predictors (Multicollinearity) in Regression using Regularization and PCA.
- After poor logistic regression performance, what improvements or alternative methods would you consider for better…
- As a Facebook data scientist, how would you reduce harmful ads despite their high revenue contribution?
- Assume we have N measurements from a single variable that we assume follows a Gaussian distribution. How do we find…
- Can you explain the idea behind singular eigenvalues, along with left singulars and right singulars, in linear…
- Can you write a query to test if frequent job changers reach data science manager roles faster?
Data_Processing
- Are there any problems with splitting data randomly into Training, Validation, and Test datasets?
- Are there any troubles when using Early Stopping?
- Can Data Cleaning worsen the results of Statistical Analysis?
- Can you use different Normalization methods on different features?
- Compare Causation vs Correlation
- Does Redundant data affect an SVM-based classifier?
Autoencoders
Cost_Function
- Can you describe a scenario in which a theoretically correct cost function might not reflect the practical business objective, and how you would reconcile the two in a real-world system?
- Can you explain how you might modify a regression loss function to explicitly penalize large negative predictions more than large positive ones in a revenue forecasting scenario?
- Describe how the shape of the cost function surface (convex vs. non-convex) can affect the optimization process. Give an example of a model or setting where non-convexity might be beneficial.
- Discuss how you would debug a situation where your training loss decreases steadily, but your validation metric (e.g., accuracy, F1-score) does not improve. How does this relate to the choice or definition of the cost function?
- Explain how you might design a custom loss function for a problem with unusual constraints (e.g., ordinal classification with specific label relationships). What pitfalls should you watch out for?
- Explain the concept of a loss function’s ‘gradient Lipschitz constant.’ Why is this property important for convergence guarantees in gradient-based methods?
Anomaly_Detection
- Can you describe the three main classifications of anomaly detection methods?
- Could you describe the core concept of the Isolation Forest algorithm, highlight its benefits for outlier detection, and explain the process of applying it to identify anomalies in data?
- How can autoencoders be leveraged for detecting unusual or outlying patterns in data?
- How can Independent Ensemble Methods be utilized to detect anomalies in data?
- How can Mahalanobis distance serve as an approach for detecting anomalies in a dataset?
- How can one-class SVM be applied for uncovering anomalies within a dataset?
Classification
- Could you compare the pros and cons of different classification algorithms, and how would you select the most suitable one in practice?
- Could you describe the essence and purpose of a confusion matrix used in classification tasks?
- Could you discuss the meaning of the F-score, and how one should interpret its numerical outcomes?
- Could you explain in detail how K-Nearest Neighbors differs from the K-means Clustering method?
- Discuss various classification evaluation metrics and explain the contexts in which each one is most applicable.
- How are Random Oversampling and Random Undersampling different, and in which scenarios are they typically used?
BiasVariance
- Could you explain the nature of the bias-variance tradeoff in machine learning and suggest approaches to address excessive bias?
- How can we provide a conceptual understanding of the balance between bias and variance when building predictive models?
- How can we recognize when a model exhibits high variance, and what techniques can we use to correct it?
- How can we set up and manage the initialization of model parameters, such as weights and biases, when working with PyTorch?
- How do Bagging and Boosting methods differ in the field of ensemble learning?
- How do Content-Based approaches differ from Collaborative Filtering methods regarding bias and variance?
K-Nearest_Neighbors
- Does the K-Nearest Neighbors method experience difficulties due to the Curse of Dimensionality, and if so, what leads to those challenges?
- How are K-Nearest Neighbors and Support Vector Machines different in their fundamental methods, and when might one approach surpass the other?
- How do Decision Trees differ from k-Nearest Neighbors, and in what ways can they be compared with respect to performance and interpretability?
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