Identifying Anomalies with Unsupervised Machine Learning Techniques
In the realm of data analysis, outliers — data points that deviate significantly from the norm — can distort analysis and affect modeling. Two popular methods for identifying these anomalies are Local Outlier Factor (LOF) and Gaussian Mixture Models (GMM), both available in the Scikit-Learn library.
Local Outlier Factor (LOF)
Local Outlier Factor (LOF) is a density-based method that detects outliers by comparing the local density around a data point to the local densities of its neighbours. This algorithm, found in the sklearn.neighbors module, calculates a local reachability density for each point using the distances to its k nearest neighbours, and then computes an outlier score (LOF score) based on how much lower this density is compared to that of its neighbours. Points in sparser regions relative to their neighbours get high LOF scores and are considered outliers.
The amount of contamination of the data set, i.e. the proportion of outliers in the data set, is a parameter in LOF. The threshold value for outliers can be calculated as a percentile, with the example given being 9%. The full code for the demonstrated methods can be found on GitHub.
Gaussian Mixture Models (GMM)
GMM, on the other hand, is a probabilistic model-based method that fits the data with a mixture of Gaussian distributions. Each component represents a cluster with a Gaussian distribution. Outliers are detected as points with very low probability of belonging to any of the Gaussian components.
GMM can be imported and fitted in Python using the sklearn module. It divides data into n groups by calculating and creating n Gaussian distributions, then placing each observation in the group with the highest probability. GMM from sklearn calculates scores for observations according to the densities of where each point is located, with points in areas of higher density less likely to be outliers and vice versa.
Use Cases
While both LOF and GMM are effective in detecting outliers, they excel in different scenarios. LOF focuses on local density differences to find points isolated from their neighbours, suitable for detecting local outliers in complex data. GMM, on the other hand, fits a global model to the data distribution and treats points with low membership probability to any cluster as outliers, which works well when data clusters have Gaussian-like shapes.
In a dataset, most observations fall within a certain range of values and follow patterns, known as inliers. Outliers are those that deviate significantly from these patterns. Other algorithms for finding outliers, such as Isolation Forest, Z-Score, and IQR, also exist.
An Example: The '_car_crashes_' Dataset
The '_car_crashes_' dataset from the seaborn package in Python can be used with LOF. This dataset contains information about car accidents, including variables such as the number of vehicles involved, the type of vehicles, and the location of the accidents. By applying LOF to this dataset, we can identify areas where car accidents are more likely to occur and potentially take preventive measures.
Linear Regression and Outliers
Linear Regression is an algorithm that is particularly affected by outliers. By identifying and removing these anomalies, we can improve the accuracy of our models and gain more reliable insights from our data.
In summary, understanding outliers and their impact on data analysis is crucial in the field of data science. By employing techniques like LOF and GMM, we can effectively detect and manage these anomalies, ensuring our models and insights are robust and reliable.
The use of Local Outlier Factor (LOF) and Gaussian Mixture Models (GMM) in the field of data science is beneficial for identifying outliers, as they can help improve the accuracy of models and provide reliable insights. LOF detects outliers by comparing a data point's local density with that of its neighbors, while GMM identifies outliers by treating points with low probability of belonging to any Gaussian component as anomalies. In medical research, for instance, these methods can be applied to large datasets containing various medical conditions, processing data-and-cloud-computing resources to analyze and detect unusual patterns or rare conditions that might be masked by traditionally analyzed data.
