Outliers are tricky, but can be more than just bad data. If you know anything about statistics, you probably know what an outlier is. Outlier detection refers to the identification of a single observation that falls outside a set of observations. Typically, this would be used to refer to some type of statistical analysis where we’re using data points to draw a conclusion. In this article lets explore one of powerful PyOD library of Python and know it all about it.
So let’s start with what is Outlier ?
Identifying outliers is one of the most important and most difficult steps in data preprocessing. Outliers are the data points that do not fit in your model. Outliers can be due to mechanical error, human error, or measurement errors.
In a dataset, outliers can cause a major impact on statistical properties of the dataset, so it is always recommended to find and remove them as soon as possible.
Before you apply any machine learning algorithm to your dataset, you need to preprocess the data and clean it; otherwise, it can result in poor performance with your model.
Outlier Detection is a technique used to identify these extreme values that can corrupt our models if we don’t deal with them correctly. Outlier Detection should be performed before modeling and when there is no obvious relationship between the variables
What is PyOD ?
Outlier detection is a common data science task, with applications in numerous domains including fraud detection, system health monitoring and fault detection. For example, an outlier may indicate a malfunctioning sensor in a manufacturing process or unusual credit card activity.
However, these examples are relatively simple; the nuances of real-world datasets can make outlier detection more difficult. Luckily, there is a Python package called PyOD that can help us with this!
PyOD is a scalable Python toolkit for detecting outliers in multivariate data. It provides access to around 20 different algorithms for detecting outliers, as well as comprehensive performance evaluation tools to help you choose the best one for your dataset.
PyOD supports a wide range of outlier detection methods, including unsupervised techniques such as k-nearest neighbors, isolation forests, support vector machine-based models, local outlier factor (LOF), average k-nearest neighbors (AKNN), angle-based outlier detection (ABOD), cluster-based local outlier factor (CBLOF), histogram-base outlier detection (HBOS), kernel density estimation (KDE), one-class support vector machines (one-class SVM), principal component analysis (PCA), locally selective combination algorithm with random projection forest (LoOP), and more.
PyOD is an open-source Python toolkit for detecting outliers in multivariate data, which was released in 2017. It contains a comprehensive collection of outlier detection methods and algorithms, including:
classification: PCA, Kernel PCA, LDA, MCD and LOF;
regression: CBLOF and HBOS;
clustering-based: ABOD and KNN,
probabilistic model-based: kde, isoForest and cof;
distance-based: SOD;
and combined models.
Challenges and Advantage of PyOD :
Some of the challenges and advantages of PYOD include:
Challenges:
-Difficult to determine which algorithm is most appropriate for a particular data set
-The parameters selected for a specific data set may not be applicable to other data sets
-It is difficult to know whether the outlier results are valid or not.
Advantages:
-PyOD has been well received by the machine learning community with over 100 stars and nearly 25 forks on the GitHub repository in just under two months since its release.
-PyOD includes more than 35 state-of-the-art outlier detection algorithms with detailed documentations, interactive demos and references.
-All methods are implemented in Python, support parallelism via joblib and it is compatible with both Python 2 and 3.
Why use PyOD?
It’s easy. The API is clean and straightforward.
It supports a wide range of outlier detection models. You can choose a model that’s best suited to your data—based on its density or distance to neighbors—or even switch methods when you find one that isn’t working as well as it should be.
It speeds up your data science workflow. PyOD integrates seamlessly with other AI tools like Spark and TensorFlow, so you don’t have to spend time learning new tools or setting up new processes.
Features of PyOD ?
PyOD is a comprehensive and scalable Python toolkit for detecting outlying objects in multivariate data. It has the following features:
-Unsupervised Outlier Detection: The core module of PyOD. Provides 20+ unsupervised outlier detection algorithms.
-Supervised Outlier Detection: Wrappers around Scikit-learn, XGBoost, LightGBM, and other popular machine learning libraries.
-Outlier Ensembles: Combines different outlier detectors to use their advantages while suppressing their weaknesses.
-Preprocessing: Scalers, transforms, and tools for model building.
-Model Evaluation: Metrics and visualization tools to evaluate detection performance and explain ability of models.
Implementation of PyOD in Python :
#Importing required library
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
%matplotlib inline
import matplotlib.font_manager
from pyod.models.abod import ABOD #Angle Based Outlier Detector
from pyod.models.knn import KNN #K Nearest Neighbors
from pyod.utils.data import generate_data, get_outliers_inliers
You can refer to PyOD documentation here also a some great repo link here as well.
PyOD doesn’t offer a lot in terms of features, but it makes up for that is its ease of use. This tool allows users to handle outlier detection quickly and without having to handle the more complex calculations themselves. It also offers two different types of detection, the choice between which users need to make based on the types of data they are using. Opting for the default option is usually sufficient, but there are times where changing it might be required. Hope you liked the article at MLDots.
