`anomaly_detection`

¶

The anomaly detection toolkit identifies data points that are *different* in
some way from the rest of an input dataset. Each data point passed to a
GraphLab Create anomaly detection model is given an anomaly score from 0 to
infinity, describing how different the point is relative to the rest of the
dataset. The higher the score, the more likely a point is anomalous, according
to a given model. Often a threshold is chosen to make a final decision whether
each point is anomalous or not; this post-processing step is left to the user.

This toolkit currently includes three models, **local outlier factor** for
datasets with multiple features and independent observations, **moving
Z-score** for sequential data (typically a time series), and **bayesian changepoints**
for identifying changes in univariate data. All three of these tools
apply to *unsupervised* problems, where the user does not have training data
labeled as anonymous or typical. For cases where such training labels do exist,
please consider using the graphlab.toolkits.classification toolkit. If you
are unsure which model to use, the create function in the anomaly_detection
namespace will pick automatically based on the schema of your dataset.

Please see the documentation for this model for more details about usage. The Anomaly Detection chapter of the User Guide provides more background on anomaly detection and walks through a more in-depth example application.

## local outlier factor¶

`local_outlier_factor.create` |
Create a `LocalOutlierFactorModel` . |

`local_outlier_factor.get_default_options` |
Information about local outlier factor parameters. |

`local_outlier_factor.LocalOutlierFactorModel` |
Local outlier factor model. |

## moving z-score¶

`moving_zscore.create` |
Create a `MovingZScoreModel` model. |

`moving_zscore.get_default_options` |
Information about moving Z-score parameters. |

`moving_zscore.MovingZScoreModel` |
Identify anomalies in univariate series. |

## bayesian changepoints¶

`bayesian_changepoints.create` |
Create a BayesianChangepointsModel. |

`bayesian_changepoints.get_default_options` |
Get the default options for the toolkit `_BayesianOnlineChangepoint` . |

`bayesian_changepoints.BayesianChangepointsModel` |
Identify changepoints in a univariate time series. |