This Python question has been asked in Facebook machine-learning interviews.
More context. You are given a dataframe with a single column, var. You do not have to calculate the p-value of the test or run the test.
Machine Learning Algorithms From Scratch Problems
Problems that ask you to write an algorithm from scratch are increasingly common in machine learning and computer vision interviews. The algorithms you are asked to write are like what you’d see on sci-kit-learn.
In general, this type of question tests your familiarity with an algorithm, as well as your ability to code a bug-free version as efficiently as possible. Most importantly, they test your knowledge of ML concepts by asking you to build the algorithms from scratch. So no more writing: rfr = RandomForest(x,y)
But although this can seem intimidating, remember that 1) interviewers don’t want the most optimized version of an algorithm. Instead, interviewers want the most “vanilla” version of an algorithm that shows you understand the basics.
And 2) you don’t have to study every algorithm. Only a few fit the format of an hour-long on-site interview, as many are too complicated to break down in such a short timeframe.
These are the algorithms you should study for machine learning Python interviews:
- K-nearest neighbors
- Decision tree
- Linear regression
- Logistic regression
- K-means clustering
- Gradient descent
