Explain the bias-variance trade-off in machine learning and its significance. Can you describe the difference between supervised and unsupervised learning? Provide examples of each. What is regularization in the context of machine learning, and why is it important? How do decision trees work, and what are some methods to prevent overfitting in decision trees? Explain the K-nearest neighbors (KNN) algorithm. What are its pros and cons? What is cross-validation, and why is it used in machine learning? Describe a few different cross-validation techniques. Discuss the difference between precision and recall. How would you choose between models with different precision-recall trade-offs? What is gradient descent? How does it relate to training machine learning models? Can you explain the concept of feature engineering and its role in improving model performance? Describe the process of dimensionality reduction. When and why might you apply it in a machine learning pipeline?
