ML Interview Questions

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.

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 bias-variance tradeoff in machine learning? How does this tradeoff influence your choice of model complexity and its subsequent performance on unseen data?

Can you describe how decision trees use information gain to decide which feature to split on at each node? How does this process contribute to creating an efficient and accurate decision tree model?

Imagine you are working on a binary classification task and your dataset is highly imbalanced. Explain how you would approach evaluating your model's performance. Discuss the limitations of accuracy in this scenario and which metrics might offer more insight into your model's performance.