How to Detect Outliers in Machine Learning

In a more general context, an outlier is an individual that is what is an outlier markedly different from the norm in some respect. In general, you should try to accept outliers as much as possible unless it’s clear that they represent errors or bad data. Your outliers are any values greater than your upper fence or less than your lower fence. If a value has a high enough or low enough z score, it can be considered an outlier.

In this case, your findings can be deemed statistically significant. If, on the other hand, your statistical significance test finds a p-value greater than 0.05, your findings are deemed statistically insignificant. To evaluate the strength of your findings, you’ll need to determine if the relationship between the two variables is statistically significant. There are several different tests used to calculate statistical significance, depending on the type of data you have. We won’t go into detail here, but essentially, you run the appropriate significance test in order to find the p-value. You can read more about the different types of data visualizations in this article, but here are two that a data analyst could use in order to easily find outliers.

When using statistical indicators we typically define outliers in reference to the data we are using. We define a measurement for the “center” of the data and then determine how far away a point needs to be to be considered an outlier. For example, when measuring blood pressure, your doctor likely has a good idea of what is considered to be within the normal blood pressure range. If they were looking at the values above, they would identify that all of the values that are highlighted orange indicate high blood pressure. There is not a hard and fast rule about how much a data point needs to differ to be considered an outlier. As a result, there are a number of different methods that we can use to identify them.

The tale of the extreme data

Sometimes, outliers result from an error that occurred during the data collection process. If it’s obvious that an outlier results from a data collection error, it’s safe to remove it. You might also choose to re-measure the data point if you can. I have a Masters of Science degree in Applied Statistics and I’ve worked on machine learning algorithms for professional businesses in both healthcare and retail.

Likewise, if the box skews closer to the minimum-valued whisker, the prominent outlier would then be the maximum value. Box plots can be produced easily using Excel or in Python, using a module such as Plotly. Standard Deviation Method is based on the assumption that data follows a normal distribution. Outliers are defined as those observations that lie beyond a specified number of standard deviations away from the mean.

• Anomalies can be indicative of novel, rare, or unexpected events.
• The interquartile range (IQR) tells you the range of the middle half of your dataset.
• This article explains what subsets are in statistics and why they are important.
• For example, when measuring blood pressure, your doctor likely has a good idea of what is considered to be within the normal blood pressure range.
• Typically, data points outside of three standard deviations from the mean are considered outliers.

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The following example shows how to interpret box plots with and without outliers. When you collect and analyze data, you’re looking to draw conclusions about a wider population based on your sample of data. For example, if you’re interested in the eating habits of the New York City population, you’ll gather data on a sample of that population (say, 1000 people).

The visualization of the scatter will show outliers easily—these will be the data points shown furthest away from the regression line (a single line that best fits the data). As with box plots, these types of visualizations are also easily produced using Excel or in Python. In statistics, data science, and machine learning, the terms “outlier” and “anomaly” are often used interchangeably. However, they represent distinct concepts that are crucial for data analysis. Understanding the difference between these two terms is essential for accurate data interpretation and effective problem-solving.

What is Outlier Detection?

This article will explain how to detect numeric outliers by calculating the interquartile range. Yet there are many ways to detect and correct the outliers but I covered the basic and important techniques once. It just uses the median rather than the mean and is less sensitive to outliers.

They can also indicate an anomaly or something of interest to study since it’s not always possible to determine if outliers are in error. Although the effects of outliers can skew results of statistics, it is rare that they are entirely removed from results without observations. Outliers are extreme values that differ from most other data points in a dataset. They can have a big impact on your statistical analyses and skew the results of any hypothesis tests. It is commonly used for univariate data analysis where the distribution can be assumed to be approximately normal.

What is an Outlier? Definition and How to Find Outliers in Statistics

But each outlier has less of an impact on your results when your sample is large enough. The central tendency and variability of your data won’t be as affected by a couple of extreme values when you have a large number of values. Now that you know what quartiles and the interquartile range are, let’s go through a step-by-step example of using the outlier equation. We’ll use a sample data set containing just 10 data points for this example. Thus, the observations with values of 1.1 and 23.5 are both labeled as outliers in the box plot since they lie outside of the lower and upper boundaries.

You can convert extreme data points into z scores that tell you how many standard deviations away they are from the mean. The average is much lower when you include the outlier compared to when you exclude it. Your standard deviation also increases when you include the outlier, so your statistical power is lower as well. This data point is a big outlier in your dataset because it’s much lower than all of the other times.

The box plot on the left for team A has no outliers since there are no tiny dots located outside of the minimum or maximum whisker. Algorithm is sensitive to outliers, since a single mislabeled example dramatically changes the class boundaries. Anomalies affect the method significantly, because k-NN gets all the information from the input, rather than from an algorithm that tries to generalize data. You must be wondering that, how does this help in identifying the outliers?