The interviewer asked about random forest and how it works. When I said that each decision tree in the forest considers a random subset of features, he disrespectfully interrupted me and told me that I am wrong. Then he scolded me for giving the "wrong" answer.
If you're trying to predict the gender of your customers and you only have 100 data points, what are possible problems?
Q: Given a function with inputs --an array with N randomly sorted numbers, and an int K, return output in an array with the K largest numbers. Q: 1. How does GMM/HMM work 2. Name some dimensional reduction method; I said PCA and we talked a bit about how PCA works and what's the physical intuiation 3. How K-means work, what kind of distance metric would you choose, what if different features have different dynamic range 4. How GMM works (EM algorithm)
En quel animal vous réincarneriez-vous ?
Model Deployment. Credit Risk(A lot of credit risk concept) Risk Modeling.
You have a database of customer transactions and for some users you don’t have much data (1-2 transactions), and you want them to use Revolut’s services more. How would you analyse the data to do so, with such limited data on some users.
- A Bays textbook example (fair and unfair coins) - Some python simple algorithm codes and calculating the complexity of the method - Some ML questions about different methods and how the learning happens and objective functions ....
Nothing was unexpected
Explain Python.
are you ready for Miami? (repeatedly)
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