Machine Learning for Beginners in Python: How to Detect Outliers

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Detecting Outliers

Preliminaries


import numpy as np
from sklearn.covariance import EllipticEnvelope
from sklearn.datasets import make_blobs

Create Data


X, _ = make_blobs(n_samples = 10,
                  n_features = 2,
                  centers = 1,
                  random_state = 1)


X[0,0] = 10000
X[0,1] = 10000

Detect Outliers

EllipticEnvelope assumes the data is normally distributed and based on that assumption “draws” an ellipse around the data, classifying any observation inside the ellipse as an inlier (labeled as 1) and any observation outside the ellipse as an outlier (labeled as -1). A major limitation of this approach is the need to specify a contamination parameter which is the proportion of observations that are outliers, a value that we don’t know.


outlier_detector = EllipticEnvelope(contamination=.1)


outlier_detector.fit(X)


outlier_detector.predict(X)
array([-1,  1,  1,  1,  1,  1,  1,  1,  1,  1])

 

Python Example for Beginners

Two Machine Learning Fields

There are two sides to machine learning:

  • Practical Machine Learning:This is about querying databases, cleaning data, writing scripts to transform data and gluing algorithm and libraries together and writing custom code to squeeze reliable answers from data to satisfy difficult and ill defined questions. It’s the mess of reality.
  • Theoretical Machine Learning: This is about math and abstraction and idealized scenarios and limits and beauty and informing what is possible. It is a whole lot neater and cleaner and removed from the mess of reality.

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