// Code Glimpse

Understanding Outlier Detection In Machine Learning

Introduction

Outlier detection is a fundamental step in the preprocessing phase of many Machine Learning applications and can have a significant impact on the results. An Outlier is an observation that lies an abnormal distance from other values in a random sample from a population.

Python Libraries for Outlier Detection

There are several libraries available in Python for outlier detection. Some of the popular ones include:

  1. PyOD: Python Outlier Detection (PyOD) is a popular Python library for detecting outliers in multivariate data. It integrates well with other scientific Python libraries like numpy, scikit-learn, and matplotlib.

  2. Sklearn: scikit-learn is a powerful Python library for Machine Learning. It provides several tools for outlier detection.

Demonstration: Outlier Detection using Local Outlier Factor (LOF)

Local Outlier Factor (LOF) is an algorithm for identifying density-based local outliers. It's based on the concept of a local density, where locality is defined by k nearest neighbours, whose distance is used to estimate the density.

Below is a simple demonstration of the LOF method on a random 2D dataset using the scikit-learn library in Python.

from sklearn.neighbors import LocalOutlierFactor
import numpy as np
import matplotlib.pyplot as plt

# generate random two dimensional data
X = np.random.RandomState(42).uniform(-4, 4, size=(20, 2))

# apply the LOF model
clf = LocalOutlierFactor(n_neighbors=20, contamination=0.1)
y_pred = clf.fit_predict(X)

# plot the level sets of the decision function
plt.title("Local Outlier Factor (LOF)")
plt.scatter(X[:, 0], X[:, 1], color='k', s=3., label='Data points')
radius = (clf.negative_outlier_factor_.max() - clf.negative_outlier_factor_) / (clf.negative_outlier_factor_.max() - clf.negative_outlier_factor_.min())
plt.scatter(X[:, 0], X[:, 1], s=1000 * radius, edgecolors='r', 
            facecolors='none', label='Outlier scores')

plt.xlabel("Feature 1")
plt.ylabel("Feature 2")
plt.legend()
plt.show()

In the above code, LocalOutlierFactor is used to create a model that identifies the outliers in the dataset. The fit_predict method is used to fit the model and predict the outliers.

Conclusion

Outlier detection is an essential part of any data preprocessing stage in machine learning. In this blog post, we discussed what outliers are and how to detect them using the Local Outlier Factor method in Python. We also discussed some of the Python libraries used for outlier detection. Always remember that the choice of the method to use for outlier detection should depend on the characteristics of the dataset under study.