From bank fraud to preventative machine maintenance, anomaly detection is an incredibly useful and common application of machine learning. The isolation forest algorithm is a simple yet powerful choice to accomplish this task. You can run the code for this tutorial for free on the ML Showcase. An outlier is nothing but a data point that differs significantly from other data points in the given dataset. Anomaly detection is the process of finding the outliers in the data, i.e. points that are significantly different from the majority of the other data points.
To access the full code please checkout my git hub repository here. An outlier is an observation in a data set that is unusually different from all other observations. In the above example, we have age data, and the outlier over here is 150 because a person having the age of 150 is impossible. Outliers can either be a mistake or just a variance in the dataset. The most common way to identify outliers is the observation that is far from the rest of the observation or far from the mean.
One of the most important steps in data pre-processing is outlier detection and treatment. Machine learning algorithms are very sensitive to the range and distribution of data points. Data outliers can deceive the training process resulting in longer training times and less accurate models. Outliers are defined as samples that are significantly different from the remaining data. Those are points that lie outside the overall pattern of the distribution.
"So unexpected was the hole that for several years computers analyzing ozone data had systematically thrown out the readings that should have pointed to its growth." According to Wikipedia, an outlier is an observation point that is distant from other observations. This definition is vague because it doesn't quantify the word "distant". In this blog, we'll try to understand the different interpretations of this "distant" notion. We will also look into the outlier detection and treatment techniques while seeing their impact on different types of machine learning models.
When modeling, it is important to clean the data sample to ensure that the observations best represent the problem. Sometimes a dataset can contain extreme values that are outside the range of what is expected and unlike the other data. These are called outliers and often machine learning modeling and model skill in general can be improved by understanding and even removing these outlier values. In this tutorial, you will discover outliers and how to identify and remove them from your machine learning dataset. Discover data cleaning, feature selection, data transforms, dimensionality reduction and much more in my new book, with 30 step-by-step tutorials and full Python source code. How to Use Statistics to Identify Outliers in Data Photo by Jeff Richardson, some rights reserved.