DevConf.CZ 2020

In today's ML world we are gathering and analyzing an enormous amount of data. But how to deal when there is too much information, i.e. too many variables? We can use grid search that will select variables for us, but this process is very computationally expensive. In my talk I will show various strategies for variable selection and how to combine them into data cleaning pipelines. I will cover univariate variable selection, PCA (Principal Component Analysis) and penalization regression technique. This talk will give you practical tips on how to get the most of your data instead of getting lost in variables.