write a code in R/SQL: Given a table with three column, (id, category, value) and each id has 3 or less category (price, size, color). Now, how can I find those id's for which the value of two or more category matches to one another? For eg: ID1 (price 10, size M, color Red), ID2 (price 10, Size L, Color Red) , ID3 (price 15, size L, color Red) Then the output should be two rows: ID1 ID2 and ID2 ID3
- What is over-fitting? How do you avoid it? - What types of regularization do we have? Which one is simpler to use? L1 or L2? - Explain decision trees? What are different metrics to classify dataset? - What is bagging? - We have two models, one with 85% accuracy, one 82%. Which one do you pick? - What is p-value and how can we use it?
Assumption of Linear Regression, etc.
Mostly technical / coding questions with limited time to answer, talk about past projects (with techncial depth), almost no questions with respect to data science (statistics, ml, math, modeling ...)
lunghezza media segmento piu' corto di uno spaghetto rotto in due casualmente.
Alice ha 9 monete, Bob ha 8 monete. Quale è la probabilità che Alice ottenga più teste di Bob?
Can you explain what regularization is. What's the difference between L1/L2 regularization
How to measure distance between data point?
Interviewer: What is the technique to evaluate Logistic regression. I answered Cross entropy. Interviewer: no
How would you respond to being asked to lead a group project you don't have time for?
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