Machine Learning

Explain the importance of continuous model monitoring and retraining in the life cycle.

Explain the concept of clustering in machine learning. How does it differ from other tasks like classification and regression?

Explain the concept of logistic regression in classification. How does it model the probability of class membership?

Explain the concept of ensemble learning in machine learning. What is the underlying idea behind ensemble methods?

Define principal components in the context of PCA. How are they calculated from the original data?

Describe the stages of the machine learning life cycle, from data collection to model deployment.

Explain the concept of distribution model-based clustering. What are Gaussian Mixture Models (GMMs), and how are they used in this context?

Describe decision tree classification and how it constructs a tree-based model for classification tasks.

Explain the averaging technique in ensemble learning. How does it combine predictions from multiple models?

Differentiate between projection-based dimensionality reduction and manifold learning. What are their primary objectives?

Define supervised learning and give examples of tasks that can be solved using this approach.

Describe hierarchical clustering and its ability to create hierarchical structures of clusters. How does agglomerative hierarchical clustering work?

Discuss the advantages and disadvantages of decision trees in handling complex decision boundaries.

Describe the max voting technique in ensemble learning. How does it work in the context of classification?

Discuss the significance of the VC dimension in understanding the generalization ability of learning algorithms.

Explain bagging and pasting as ensemble techniques. What are the key differences between them?