For linear regression, what are some of the assumptions a data scientist is most likely to make?
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You are given two tables- friend_request and request_accepted. Friend_request contains requester_id, time and sent_to_id and request_accepted table contains time, acceptor_id and requestor_id. How will you determine the overall acceptance rate of requests?
What is the difference between bayesian estimate and maximum likelihood estimation (mle)?
Could you draw a comparison between overfitting and underfitting?
Explain the decision tree algorithm?
Explain the term binomial probability formula?
What do you understand by clustering?
What is the difference between supervised learning an unsupervised learning?
What are numpy, scipy, and spark essential datatypes?
What do you understand by Ordinary Least Squares Linear Regression?
Which one would you prefer for text analytics python or r?
What is nosql? Name some examples of nosql databases.
What is meant by feature vectors?
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