Anomaly Detection Model and Prediction

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.