1. Aprender
    2. /
  2. Cursos
    3. /
  3. Anomaly Detection in Python
  • 1

    Detecting Univariate Outliers

    Gratuito

    This chapter covers techniques to detect outliers in 1-dimensional data using histograms, scatterplots, box plots, z-scores, and modified z-scores.

  • 2

    Isolation Forests with PyOD

    In this chapter, you’ll learn the ins and outs of how the Isolation Forest algorithm works. Explore how Isolation Trees are built, the essential parameters of PyOD's IForest and how to tune them, and how to interpret the output of IForest using outlier probability scores.

  • 3

    Distance and Density-based Algorithms

    After a tree-based outlier classifier, you will explore a class of distance and density-based detectors. KNN and Local Outlier Factor classifiers have been proven highly effective in this area, and you will learn how to use them.

  • 4

    Time Series Anomaly Detection and Outlier Ensembles

    In this chapter, you’ll learn how to perform anomaly detection on time series datasets and make your predictions more stable and trustworthy using outlier ensembles.

Initializing

Exercicio

Choosing contamination

Even though the code implementation only takes a few lines, finding the suitable contamination requires attention.

Recall that contamination parameter only affects the results of IForst. Once IForest generates raw anomaly scores, contamination is used to chose the top n% of anomaly scores as outliers. For example, 5% contamination will choose the observations with the highest 5% of anomaly scores as outliers.

Although we will discuss some tuning methods in the following video, for now, you will practice setting an arbitrary value to the parameter.

The data is loaded as big_mart.

Instruções

100 XP
  • Instantiate an IForest() estimator with 5% contamination.
  • Fit the instance to the Big Mart sales data.