This blog post will show you how to find outliers in your machine learning data using Python and the scikit-learn library.
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Introduction: How to Find Machine Learning Outliers
In this post, we’ll discuss how to find outliers in machine learning data. We’ll start with a brief discussion of what outliers are and why they’re important. We’ll then talk about some of the methods used to detect outliers in machine learning data. Finally, we’ll discuss some of the potential problems with outlier detection in machine learning.
Why Find Outliers?
There are many reasons why you might want to find outliers in your data. Maybe you’re looking for unusual patterns that could indicate fraud or error. Maybe you want to clean up your data set before training a machine learning model. Or maybe you’re just curious and want to see what kind of strange things are hiding in your data set!
Whatever your reason, there are a few different ways to go about finding outliers in machine learning data. In this post, we’ll take a look at a few of the most common methods and see how they work in practice.
How to Find Outliers
An outlier is an observation point that is distant from other observations. Outliers can occur in either a univariate or multivariate setting. In a univariate setting, an outlier is defined as a point that is greater than three standard deviations from the mean. In a multivariate setting, an outlier is defined as a point that is more than three standard deviations from the mean in at least one direction.
There are various ways to find outliers in your data, but the most common method is to use a technique called z-score normalization. Z-score normalization transforms your data so that the mean is 0 and the standard deviation is 1. This transformation allows you to easily compare values and find outliers.
To find outliers using z-score normalization, you first need to calculate the z-scores for each observation in your data. You can do this by subtracting the mean from each value and then dividing by the standard deviation. Once you have calculated the z-scores, you can identify outliers by looking for values that are greater than 3 or less than -3.
Another method for finding outliers is to use authority limits. Authority limits are defined as points that are more than 2 standard deviations from the mean in at least one direction. To find authority limits, you first need to calculate the z-scores for each observation in your data. You can do this by subtracting the mean from each value and then dividing by the standard deviation. Once you have calculated the z-scores, you can identify outliers by looking for values that are greater than 2 or less than -2 .
Outlier Detection Techniques
There are dozens of outlier detection techniques ranging from simple statistical methods to more complex machine learning models. No technique is perfect and each has its own advantages and disadvantages. The key is to select the right technique for your data and your specific outlier detection task.
One of the simplest outlier detection techniques is to compute the z-score for each data point. The z-score is calculated as:
z = (x – μ) / σ
where x is the data point, μ is the mean, and σ is the standard deviation. Data points with a z-score greater than 3 or less than -3 are considered outliers.
Another common technique is to compute the median absolute deviation (MAD). The MAD is calculated as:
MAD = median(|x – median(x)|)
Data points with a value greater than 1.4826 * MAD are considered outliers.
Other outlier detection techniques include:
-Clustering: Data points that are far from the other points in their cluster are considered outliers. This technique works well if there is a clear clustering structure in the data. PCA can be used to reduce the dimensionality of the data before applying a clustering algorithm. K-means clustering is a popular choice for detecting outliers in high dimensional data. Local Outlier Factor (LOF) can also be used for this purpose. LOF identifies areas of the feature space that have a density of points that is much lower than neighboring areas, and these low density areas are likely to contain outliers. DBSCAN can be used to find clusters of any shape, even in high dimensional data, but it requires setting two parameters which can be difficult to tune. OPTICS can be used as an alternative to DBSCAN that does not require setting parameters, but it does not work well in high dimensional data. Hierarchical clustering can also be used for outlier detection by cutting off The bottom portion of the dendrogram produced by Hierarchical clustering algorithms such as AgglomerativeClustering(). Any points that do not belong to any cluster after cutting off The bottom portion of The dendrogram are considered outliers. This method works well if there are many small clusters in The data and if The goal is to find outliers in each cluster separately rather than finding global outliers across all of The data. This method will not work well if there are only a few large clusters because cutting off The bottom portion of The dendrogram will remove all but one of The clusters which will not leave any outliers remaining.’),)
Finally, we looked at a few different ways to find outliers in machine learning data. We started with a simple approach, using the mean and standard deviation to identify outliers. We then looked at two more sophisticated methods, the interquartile range and the median absolute deviation. We also looked at a method for finding outliers in time series data.
Keyword: How to Find Machine Learning Outliers