Danil

How to use inverse transform sampling to improve your modelNormalizing data is a common task in data science. Sometimes it allows us to speed up gradient descent or improve model accuracy, and in some cases it absolutely crucial. For example, the model I described in my last article cannot handle targets that are distributed non-normally. Some normalization techniques, like taking a logarithm, may work most of the time, but in this case, I decided to try something that would work for any data, no matter how it was…

Make predictions with pure statisticsPhoto by Nika Benedictova on UnsplashThis article is in some sense a sequel to my previous article on Bayesian statistics. Long story short, the company I work for needed a model that not only predicts a target variable as a number but also estimates its probability density, i.e. estimates that the predicted variable will likely be in a certain range from the prediction. As a result, the model based on the naive Bayes approach has been created that not only suited the initial task but…