I totally forgot about writing some new stuff here, but recently I found these 160 data science interview questions on Hackernoon, and decided to try to answer each one of them in order to force me to study all of those interesting topics. I will post my answers (hopefully, right and comprehensible) trying to write ~2⁄3 answers each couple of days.
If you spot anything wrong, contact me please!
What is supervised machine learning
Is the task of finding a function that maps from the input features of the data to the output feature. When The output variable is continuous, it’s called regression, when it’s categorical is called classification. Examples are linear regression for the former and a decision tree for the latter.
What is regression, which models can you use to solve a regression problem
Regression is the task of finding a function that maps from the input features of a dataset to the continous output feature. This allows prediction of each possible input to an output. Polynomial regression and neural networks are examples to solve a regression problem.
What is linear regression, when do we use it
A linear regression is a machine learning algorithm that solves a regression problem. It finds the line that minimizes some metric, usually the sum of all squared error of each input point (but other metrics can be applied). We use it in regression problems where the whole dataset is made of continuous variables, and with a single output.