Welcome to your quiz on Machine Learning Marathon. All the best!

What’s the trade-off between bias and variance?

How is KNN different from k-means clustering?

Explain how a ROC curve works.

Define precision and recall.

What is Bayes’ Theorem? How is it useful in a machine learning context?

Why is “Naive” Bayes naive?

Explain the difference between L1 and L2 regularization.

What’s your favorite algorithm, and can you explain it to me in less than a minute?

What’s the difference between Type I and Type II error?

What’s a Fourier transform?

What cross-validation technique would you use on a time series dataset?

Which is more important to you– model accuracy, or model performance?

What’s the F1 score? How would you use it?

How would you handle an imbalanced dataset?

How do you ensure you’re not overfitting with a model?

What evaluation approaches would you work to gauge the effectiveness of a machine learning model?

Which data visualization libraries do you use? What are your thoughts on the best data visualization tools?

What are parametric models? Give an example.

What is the "Curse of Dimensionality?" Also, how do you address it?

What is the difference between stochastic gradient descent (SGD) and gradient descent (GD)?

When would you use GD over SDG, and vice-versa?

What is the Box-Cox transformation used for?

What are 3 data preprocessing techniques to handle outliers?

What are 3 ways of reducing dimensionality?

What are the advantages and disadvantages of decision trees?

What are the advantages and disadvantages of neural networks?

How can you choose a classifier based on training set size?

Explain Latent Dirichlet Allocation (LDA).

Explain Principle Component Analysis (PCA).

What is the ROC Curve and what is AUC (a.k.a. AUROC)?

Why is Area Under ROC Curve (AUROC) better than raw accuracy as an out-of-sample evaluation metric?

Why are ensemble methods superior to individual models?

Explain bagging

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