Covariate Shift – Unearthing hidden problems in Real World Data Science

Introduction You may have heard from various people that data science competitions are a good way to learn data science, but they are not as useful in solving real world data science problems. Why do you think this is the case? One of the differences lies in the quality of data that has been provided. In Data Science Competitions, the datasets are carefully curated. Usually, a single large dataset is split into train and test file. So, most of the times the train and test have been generated from the same distribution. But this is not the case when dealing with real world problems, especially when the data has been collected over a long period of time. In such cases, there may be multiple variables / environment changes might have happened during that period. If proper care is not taken then, the training dataset cannot be used to predict anything about the test dataset in a usable manner. In this article, we will see the different types of problems or Dataset Shift that we might encounter in the real world. Specifically, we will be talking in detail about one particular kind of shift in the Dataset (Covariate shift), the existing methods…


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