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?
what is your salary expectation?
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 ...)
Interviewer: What is the technique to evaluate Logistic regression. I answered Cross entropy. Interviewer: no
Given measurements of acceleration taken from a wristband, with second by second acceleration in the X, Y, Z axis, how would you predict if the person wearing the wrist is sitting, walking or just standing?
Mettez vous à jour des procédures?
Let's say you finished developing a machine learning model and you are getting 95% accuracy. Should you be happy?
From the csv files, how is "A" improved over "B"? Do you have any other findings?
How would you respond to being asked to lead a group project you don't have time for?
Viewing 341 - 350 interview questions