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Can you give an overview of Support Vector Machine?

Which characteristics of a data visualization are necessary for making it worth analyzing?

Are you able to describe the contrast between likelihood and probability?

Can you tell me about a time when you made a decision that didn't work out? What did you learn as a result of this?

What makes SVMs a preferred choice in some cases, and what are their shortcomings?

How would you describe the time complexity characteristic of SVM training? Can you describe the time complexity for the inference phase in SVMs? Can you compare the time complexities for training and inference in SVMs with linear and RBF kernels?

Can you give me an example of a successful prediction you've made for a user's lifetime value?

What do you think would be the biggest challenge you'd face in the Capital One Data Scientist role?

I want to know what you learned from your major failure at work.

In what ways would you measure success?