A value that “lies outside” (is much smaller or larger than) most of the other values in a set of data. For example in the scores 25,29,3,32,85,33,27,28 both 3 and 85 are “outliers”.

An outlier is a number that is at least 2 standard deviations away from the mean. For example, in the set, 1,1,1,1,1,1,1,7, 7 would be the outlier.

A commonly used rule says that a data point is an outlier if it is more than 1.5 ⋅ IQR 1.5/cdot /text{IQR} 1. 5⋅IQR1, point, 5, dot, start text, I, Q, R, end text above the third quartile or below the first quartile.

Definition of outliers. An outlier is an observation that lies an abnormal distance from other values in a random sample from a population. In a sense, this definition leaves it up to the analyst (or a consensus process) to decide what will be considered abnormal.

The three different types of outliers

• Type 1: Global outliers (also called “point anomalies”):
• Type 2: Contextual (conditional) outliers:
• Type 3: Collective outliers:
• Global anomaly: A spike in number of bounces of a homepage is visible as the anomalous values are clearly outside the normal global range.

While anomaly is a generally accepted term, other synonyms, such as outliers, discordant observations, exceptions, aberrations, surprises, peculiarities or contaminants, are often used in different application domains. In particular, anomalies and outliers are often used interchangeably.

An outlier may be due to variability in the measurement or it may indicate experimental error; the latter are sometimes excluded from the data set. Outliers can occur by chance in any distribution, but they often indicate either measurement error or that the population has a heavy-tailed distribution.

Arbitrarily set outliers fraction as 1% based on trial and best guess. Fit the data to the CBLOF model and predict the results. Use threshold value to consider a data point is inlier or outlier. Use decision function to calculate the anomaly score for every point.

With respect to regression, outliers are influential only if they have a big effect on the regression equation. Sometimes, outliers do not have big effects. For example, when the data set is very large, a single outlier may not have a big effect on the regression equation.

5 ways to deal with outliers in data

1. Set up a filter in your testing tool. Even though this has a little cost, filtering out outliers is worth it.
2. Remove or change outliers during post-test analysis.
3. Change the value of outliers.
4. Consider the underlying distribution.
5. Consider the value of mild outliers.

Dealing with Outliers

1. Option 1 is to delete the value. If you have only a few outliers, you may simply delete those values, so they become blank or missing values.
2. Option 2 is to delete the variable.
3. Option 3 is to transform the value.
4. Option 4 is to transform the variable.

Outliers: To Drop or Not to Drop

• If it is obvious that the outlier is due to incorrectly entered or measured data, you should drop the outlier:
• If the outlier does not change the results but does affect assumptions, you may drop the outlier.
• More commonly, the outlier affects both results and assumptions.

An outlier can affect the mean of a data set by skewing the results so that the mean is no longer representative of the data set.

One method is to remove outliers as a means of trimming the data set. Another method involves replacing the values of outliers or reducing the influence of outliers through outlier weight adjustments. The third method is used to estimate the values of outliers using robust techniques.