![]() ![]() Outliers can skew statistical measures and data distributions, providing a misleading representation of the underlying data and relationships. It can be important to identify and remove outliers from data when training machine learning algorithms for predictive modeling. In this case, simple statistical methods for identifying outliers can break down, such as methods that use standard deviations or the interquartile range. This is easy to understand when we have one or two variables and we can visualize the data as a histogram or scatter plot, although it becomes very challenging when we have many input variables defining a high-dimensional input feature space. Perhaps the most common or familiar type of outlier is the observations that are far from the rest of the observations or the center of mass of observations. Outliers are observations in a dataset that don’t fit in some way. This tutorial is divided into three parts they are: Photo by Zoltán Vörös, some rights reserved. Model-Based Outlier Detection and Removal in Python ![]() Kick-start your project with my new book Data Preparation for Machine Learning, including step-by-step tutorials and the Python source code files for all examples. How to evaluate and compare predictive modeling pipelines with outliers removed from the training dataset.How to correctly apply automatic outlier detection and removal to the training dataset only to avoid data leakage. ![]() Automatic outlier detection models provide an alternative to statistical techniques with a larger number of input variables with complex and unknown inter-relationships. ![]() In this tutorial, you will discover how to use automatic outlier detection and removal to improve machine learning predictive modeling performance.Īfter completing this tutorial, you will know: 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. Identifying and removing outliers is challenging with simple statistical methods for most machine learning datasets given the large number of input variables. The presence of outliers in a classification or regression dataset can result in a poor fit and lower predictive modeling performance. ![]()
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