Welcome to Part 2 of our Machine Learning Interview Questions Series, your step-by-step guide to mastering the next level of ML concepts commonly asked in interviews. In this installment, we go beyond the basics and explore practical and theoretical knowledge that hiring managers expect from junior to mid-level machine learning engineers.
Whether you’re preparing for your first data science interview or revisiting ML fundamentals with fresh clarity, these questions will strengthen your technical understanding and communication skills both essential for interview success.
11. What is the difference between Classification and Regression?
Both are types of supervised learning, but they solve different types of problems:
- Classification predicts discrete class labels (e.g., spam vs not spam, disease vs no disease).
- Regression predicts continuous values (e.g., predicting house price, temperature, or stock price).
Key Differences:
| Aspect | Classification | Regression |
|---|---|---|
| Output | Categories or classes | Real-valued numbers |
| Evaluation | Accuracy, F1-score, AUC | RMSE, MAE, R² |
| Example | Email spam detection | Predicting housing prices |
12. What is a Confusion Matrix?
A confusion matrix is a performance evaluation tool for classification models. It displays the number of true positives, false positives, true negatives, and false negatives.
Structure:
| Predicted Positive | Predicted Negative | |
|---|---|---|
| Actual Positive | True Positive (TP) | False Negative (FN) |
| Actual Negative | False Positive (FP) | True Negative (TN) |
From the confusion matrix, you can derive metrics like:
- Accuracy = (TP + TN) / Total
- Precision = TP / (TP + FP)
- Recall = TP / (TP + FN)
- F1-Score = 2 * (Precision * Recall) / (Precision + Recall)
These metrics give deeper insights than accuracy alone, especially when dealing with imbalanced datasets.
13. What is Bias-Variance Tradeoff in ML?
The bias-variance tradeoff explains the tension between a model’s ability to learn from data and its ability to generalize to new, unseen data.
- Bias refers to error from incorrect assumptions (e.g., underfitting).
- Variance refers to error from sensitivity to small fluctuations in training data (e.g., overfitting).
Goal: Find the balance where the model performs well on both training and test data.
A model with high bias oversimplifies the problem, while one with high variance is too complex. Techniques like regularization, cross-validation, and model selection help manage this tradeoff.
14. What is Cross-Validation?
Cross-validation is a technique to assess how a model performs on unseen data by splitting the dataset into multiple parts.
- The most common method is K-Fold Cross-Validation, where the dataset is divided into K parts.
- The model is trained K times, each time using a different fold as the validation set and the remaining K-1 as the training set.
Benefits:
- More reliable performance estimate
- Helps prevent overfitting
- Ensures all data gets used for both training and validation
15. What is Feature Scaling and why is it important?
Feature scaling ensures that all input features contribute equally to the result. Without it, models that rely on distance metrics (like KNN or SVM) may behave unpredictably if features are on different scales.
Common techniques:
- Min-Max Scaling (Normalization): Scales features to [0, 1]
- Standardization (Z-score scaling): Transforms features to have zero mean and unit variance
Models like tree-based algorithms are less sensitive to feature scaling, but it’s critical for others like logistic regression, K-means, and neural networks.
16. What is One-Hot Encoding?
One-hot encoding is a method of converting categorical variables into a binary format that ML algorithms can use.
Example:Color = [Red, Green, Blue] →
One-hot encoded =
- Red = [1, 0, 0]
- Green = [0, 1, 0]
- Blue = [0, 0, 1]
It avoids giving ordinal importance to categories and is widely used in preprocessing for models that don’t handle categorical variables natively.
17. What is Dimensionality Reduction?
Dimensionality reduction is the process of reducing the number of input variables in a dataset while retaining as much relevant information as possible.
Why it’s important:
- Reduces overfitting
- Speeds up model training
- Makes visualizations easier
Popular techniques:
- PCA (Principal Component Analysis)
- t-SNE (for visualization)
- LDA (Linear Discriminant Analysis)
It’s essential when working with high-dimensional data like images or text.
18. What is the Curse of Dimensionality?
The curse of dimensionality refers to various issues that arise when working with high-dimensional data:
- Distance metrics become less meaningful
- Data becomes sparse, making pattern recognition difficult
- More data is required to generalize properly
As dimensionality increases, model performance can degrade unless techniques like feature selection, PCA, or regularization are used.
19. What is the difference between Parametric and Non-Parametric Models?
- Parametric models assume a specific form for the function and learn parameters (e.g., Linear Regression, Logistic Regression).
- Non-parametric models make fewer assumptions and can adapt more flexibly (e.g., Decision Trees, KNN, Random Forests).
Tradeoff:
- Parametric: Simpler, faster, requires fewer data
- Non-parametric: More powerful, but can overfit and need more data
20. What is Regularization in Machine Learning?
Regularization is a technique used to reduce overfitting by penalizing large coefficients in the model.
Types:
- L1 Regularization (Lasso): Adds absolute value of coefficients
- L2 Regularization (Ridge): Adds square of coefficients
Effect:
Encourages smaller weights, reduces model complexity, and improves generalization.
Regularization is especially useful in linear models and neural networks where high variance can cause overfitting.
Conclusion
In this second part of our Machine Learning Interview Questions Series, we’ve focused on core intermediate concepts like evaluation metrics, bias-variance tradeoff, regularization and preprocessing techniques. These are the building blocks of real-world ML systems and frequently appear in both coding and theory-based interview rounds.
By mastering these topics, you’re well-prepared to demonstrate not just theoretical knowledge but also practical ML thinking that employers are looking for.
Stay tuned for Part 3, where we’ll cover algorithm comparisons, ensemble methods, model deployment basics, and more.
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Machine Learning Interview Questions – Part 1
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