The presence of outliers in a classification or regression dataset can result in a poor fit and lower predictive modeling performance.
Identifying and removing outliers is challenging with simple statistical methods for most machine learning datasets given the large number of input variables. Instead, automatic outlier detection methods can be used in the modeling pipeline and compared, just like other data preparation transforms that may be applied to the dataset.
In this tutorial, you will discover how to use automatic outlier detection and removal to improve machine learning predictive modeling performance.
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