In data science, examples are at the core of learning from data processes. If unusual, inconsistent, or erroneous data is fed into the learning process, the resulting model may be unable to generalize the accommodation of any new data correctly. An unusually high value present in a variable, apart from skewing descriptive measures such as the mean and variance, may also distort how many machine learning algorithms learn from data, causing distorted predictions as a result.
When a data point deviates markedly from the others in a sample, it is called an outlier. Any other expected observation is labeled as an inlier.
A data point may be an outlier due to the following three general causes (and each one implies different remedies):
- The point represents a rare occurrence, but it is also a possible value, given the fact that the available data...