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 correctly generalize the accommodating of any new data. 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 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 yet a possible value given the fact that the available data is just a sample of the original data distribution. In such an occurrence, the generative underlying process is the same...