Four variables make a machine learning model selected: the model parameters and their distribution, the model structure and its distribution. Changes in data implies changes in the model structure. For a complex data set one has to deal with an ensemble of selected models. An ensemble is supposed to collect different models, which fit the data holistically. This talk will discuss principles of model selection for a single model and for an ensemble.

OpenTalks.AI – ведущая независимая открытая конференция по искусственному интеллекту в России.
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