Anomaly Detection Model and Prediction

Explore how to implement the H2O Isolation Forest algorithm for anomaly detection using the EmployeeAttrition dataset. Understand key model parameters, train the estimator, and interpret results to identify anomalous employee behaviors. Gain practical skills in uncovering distinct patterns that highlight irregularities in employee data and derive actionable insights.

We'll cover the following...

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Training the H2OIsolationForestEstimator algorithm
Identifying anomalies

In this lesson, we’ll cover the implementation of the H2OIsolationForestEstimator algorithm, including how it works, the key parameters involved, and how to interpret the results.

We’ll work with the EmployeeAttrition dataset, apply the anomaly detection algorithm using H2OIsolationForestEstimator, and examine the model results. By the end of this lesson, we’ll have the knowledge and practical skills to detect anomalies in different datasets and uncover valuable insights for the business or research. Let’s dive in and learn about the model and its outcome in detail.

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Let’s take a look at our data. We’ll use the EmployeeAttrition dataset, which contains employee records with information about their employment history and attrition.

1.Introduction to Machine Learning

2.Supervised Learning: Regression Models with H2O

3.Supervised Learning: Classification Models with H2O

4.Unsupervised Learning: Clustering with H2O

5.Unsupervised Learning: Anomaly Detection with H2O

6.Closing Notes

7.Appendix

Anomaly Detection Model and Prediction

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