Anomalous data is the risk that your data is not evenly distributed or easily separable. Datasets from the real world are going to contain outliers and data that needs to be adjusted. In this recipe, we will discuss a basic technique used in data analysis to work with anomalous data and distribute the results while maintaining the data distribution.
Anomalous data
Getting ready
Outliers are a huge issue with datasets where you want to have a clean distribution of data. In terms of the generative model, we are interested in ensuring that the model can find the right representation of the distribution and model it appropriately. This recipe is going to focus on the tools you will use in these instances to solve problems with...
