DevConf.CZ 2020

Statistical learning can and should be applied to many tasks and situations. This hot topic is covered in many talks and classes which talk about machine learning models and the impressive results they can achieve. This is step two, though, the first being able to create datasets which can be used for training. It is not just about the quality and data used but also the representation. An ill-chosen representation can model convergence slow or even impossible, making the model potentially unusuable. The same applies to the representation of the model output, not all output formats are the same.
This talk will talk about the problems with the representation of features and results. The effects of bad choice are shown as well as examples from a number of different problem areas which will show how (sometimes) creative the data scientist has to be to produce a well-performing model.