1. Explain the bias-variance tradeoff.
- This question tests your understanding of model performance and generalization. You should explain how high bias can lead to underfitting and how high variance can lead to overfitting, and the need to find a balance between the two.
2. How would you handle missing data in a dataset?
- Here, you should discuss different strategies like imputation (mean, median, mode), deletion, or using algorithms that support missing values. Mentioning the impact of missing data on the model and the importance of understanding the underlying mechanism can be crucial.
3. Describe how you would validate a machine learning model.
- Discuss different validation techniques like cross-validation (k-fold, stratified), train-test split, and the importance of using unseen data to evaluate the model’s performance. Explain metrics like accuracy, precision, recall, F1-score, and ROC-AUC.
4. How do you select important features in a dataset?
- Mention methods like correlation analysis, feature importance from models (like Random Forest or Gradient Boosting), regularization techniques (Lasso, Ridge), and dimensionality reduction techniques (PCA).
5. Can you explain a machine learning algorithm you have used in a project and the results you achieved?
- Be ready to discuss a specific project in detail. Explain the problem, the dataset, the choice of algorithm, how you tuned hyperparameters, and the results. This demonstrates your practical experience and ability to apply theoretical knowledge.