ML Interview Questions

Can you explain the working mechanism of the K-Nearest Neighbors (KNN) algorithm for both classification and regression tasks? Discuss its strengths and limitations. How do you determine the optimal value of K? Additionally, elaborate on the concept of the curse of dimensionality in relation to KNN.

Explain how the random forest algorithm works and why it is often more effective than a single decision tree. Include the concepts of bagging and feature randomness in your explanation.

Explain the process of k-fold cross-validation and its significance in evaluating machine learning models.

Explain how decision trees work, including the algorithm's approach to splitting nodes and handling both categorical and continuous variables.

Explain gradient boosting algorithms. How do they work, and what are the differences between XGBoost, LightGBM, and CatBoost?

What are the main approaches to feature selection in machine learning? Discuss the advantages and disadvantages of filter, wrapper, and embedded methods.

Explain the difference between supervised and unsupervised learning, and provide examples of algorithms used in each. Additionally, discuss the types of problems each is best suited to solve.

The concept of the 'curse of dimensionality' is often mentioned in the context of machine learning and data analysis. Can you explain what this term means and discuss its implications on model training and performance? Additionally, illustrate your explanation with an example of how adding dimensions can affect a k-nearest neighbors algorithm.

Explain how Principal Component Analysis (PCA) reduces dimensionality and discuss a scenario where applying PCA might improve a machine learning model's performance. What are some of the potential drawbacks of using PCA?

Can you explain the K-means clustering algorithm, including its step-by-step process, limitations, and practical applications? Additionally, when would K-means be the most appropriate choice for clustering data?