1
Introduction
2
Understanding Anomalies
▶
2.1
Isolation Forest
2.2
Input Data for Anomalies
2.3
Interpreting BigML Anomalies
2.4
Anomalies with Images
3
Creating Anomalies with 1-Click
4
Anomaly Configuration Options
▶
4.1
Number of Anomalies
4.2
Forest Size
4.3
Constraints
4.4
ID fields
4.5
Sampling Options
4.6
Creating Anomolies with Configured Options
4.7
API Request Preview
5
Visualizing Anomalies
▶
5.1
Anomaly Visualization with Images
5.2
Create a Dataset from Anomalies
6
Anomaly Predictions: Anomaly Scores
▼
6.1
Introduction
6.2
Creating Anomaly Scores
6.3
Configuring Anomaly Scores
6.4
Visualizing Anomaly Scores
6.5
Consuming Anomaly Scores
6.6
Descriptive Information
6.7
Anomaly Scores Privacy
6.8
Moving Scores
6.9
Stopping Scores Creation
6.10
Deleting Anomaly Scores
7
Consuming Anomalies
▶
7.1
Downloading Anomalies
7.2
Using Anomalies via the BigML API
7.3
Using Anomalies via the BigML Bindings
8
Anomalies Limits
9
Anomalies Descriptive Information
▶
9.1
Anomalies Name
9.2
Description
9.3
Category
9.4
Tags
9.5
Counters
10
Anomalies Privacy
11
Moving Anomalies
12
Stopping Anomalies Creation
13
Deleting Anomalies
14
Takeaways
Bibliography
Glossary
Anomaly Detection with the BigML Dashboard
6 Anomaly Predictions: Anomaly Scores
6.1
Introduction
6.2
Creating Anomaly Scores
6.3
Configuring Anomaly Scores
6.4
Visualizing Anomaly Scores
6.5
Consuming Anomaly Scores
6.6
Descriptive Information
6.7
Anomaly Scores Privacy
6.8
Moving Scores
6.9
Stopping Scores Creation
6.10
Deleting Anomaly Scores
Create a Dataset from Anomalies
Introduction
