Preparing for a data science interview? These key questions cover foundational concepts in machine learning, statistics, and data handling. Each includes a concise explanation to help you build a strong foundation.
1.How do supervised and unsupervised learning differ?
Supervised learning relies on datasets with known outcomes to train models for tasks like forecasting or categorizing items. In contrast, unsupervised learning works with raw, unlabeled data to uncover hidden structures, such as groupings or simplified representations.
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Describe the bias-variance tradeoff. This concept highlights the challenge of balancing model simplicity (low bias for better fit to data) with flexibility (low variance for reliable predictions on new data). Tuning one often impacts the other; strategies like adding penalties, validating across folds, or expanding datasets can mitigate this.
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What does feature engineering involve? It refers to the process of crafting or refining input variables from raw data to enhance predictive accuracy. Common approaches include normalizing scales, converting categories to numbers, or generating combined features for deeper insights.
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What strategies exist for managing missing data? Options include filling gaps with averages, medians, or frequent values; dropping incomplete rows or features; leveraging algorithms that account for absences; or adding indicators to flag missing entries.
5.Why is cross-validation important?
It provides a robust estimate of how a model will perform on fresh data by repeatedly dividing the dataset into training and validation subsets, helping to detect and curb overfitting.
6.Define regularization and its role.
Regularization applies constraints (such as L1 for sparsity or L2 for shrinkage) to limit model complexity, discouraging overfitting by incorporating a cost for overly intricate patterns.
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What is a confusion matrix used for? This performance summary table for classifiers breaks down results into true positives (correctly identified instances), true negatives (correctly ruled out), false positives (errors of inclusion), and false negatives (errors of omission).
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Distinguish between precision and recall.
Precision gauges the reliability of positive identifications (true positives divided by all positives predicted). Recall measures how comprehensively positives are captured (true positives divided by all actual positives).
9.What does the F1-score represent?
It balances precision and recall through their harmonic average: ( 2 \times \frac{\text{Precision} \times \text{Recall}}{\text{Precision} + \text{Recall}} ), ideal for uneven class distributions.
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Explain ROC curves and AUC scores.
The ROC curve visualizes a model's trade-off between true positive rates and false positive rates across thresholds. AUC quantifies overall separability, with values closer to 1 indicating superior class discrimination. -
What challenges arise from the curse of dimensionality?
High-dimensional spaces demand exponentially more data for effective generalization, often resulting in sparse datasets and heightened overfitting risks. -
Outline the basics of Principal Component Analysis (PCA). PCA is a method for compressing features by projecting data onto axes that preserve the most variation, enabling efficient analysis while retaining key information.
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How can you address class imbalance in datasets? Techniques range from adjusting sample sizes (e.g., duplicating minorities or trimming majorities), weighting errors differently, treating outliers as anomalies, or selecting metrics that favor the minority class.
14.What are the core assumptions underlying linear regression?
These include a linear relationship between variables, uncorrelated residuals, constant error variance (homoscedasticity), and residuals following a normal distribution.
These include a linear relationship between variables, uncorrelated residuals, constant error variance (homoscedasticity), and residuals following a normal distribution.
- Clarify the distinction between correlation and causation. Correlation quantifies how variables vary in tandem, but it doesn't prove one influences the other—causation requires evidence like controlled experiments to confirm direct effects.
16.Summarize the Central Limit Theorem.
It states that, given sufficiently large samples, the means of those samples will form a bell-shaped normal distribution, irrespective of the original data's shape.
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What approaches work best for outlier detection and treatment? Methods include exclusion or bounding extremes, applying transformations for normalization, or employing statistics that resist outlier influence.
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What are ensemble methods, and why use them? These integrate outputs from several models (e.g., bagging in Random Forests or boosting in algorithms like XGBoost) to yield more stable and accurate results than individual learners.
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How do you assess the quality of a regression model?
Common metrics evaluate prediction errors (Mean Squared Error, Root Mean Squared Error, Mean Absolute Error) and explanatory power (R-squared). -
Name several widely used machine learning algorithms. Regression options: Linear and Logistic Regression. Tree-based: Decision Trees and Random Forests. Others: Support Vector Machines, K-Nearest Neighbors for classification, K-Means for clustering, and Hierarchical Clustering.