is a comprehensive book on getting a job at a top tech company, while focuses on dev interviews and does this for PMs.
CareerCup's interview videos give you a real-life look at technical interviews. In these unscripted videos, watch how other candidates handle tough questions and how the interviewer thinks about their performance.
Most engineers make critical mistakes on their resumes -- we can fix your resume with our custom resume review service. And, we use fellow engineers as our resume reviewers, so you can be sure that we "get" what you're saying.
Our Mock Interviews will be conducted "in character" just like a real interview, and can focus on whatever topics you want. All our interviewers have worked for Microsoft, Google or Amazon, you know you'll get a true-to-life experience.
Quite complex question, I would probably arise a conversation and answer like this: In which context you will use the decision tree? Is it a random tree in a random forest? It is also important what are your features (ordered/unordered). Lets assume that tree is already trained. At each node you have to store:
- autoboli January 27, 20151) links to children
2) dimension of a feature to be thresholded
3) a feature threshold
4) a probability vector (how many training data get to this node for each class)
Your implementation should also support training. Given the training data, dimension of a feature and threshold you will need to compute the information gain (or entropy if you will). How many data we have (bootstraping, cross validation)....