Isolation Forest is an unsupervised algorithm used for anomaly detection that isolates the anomalies rather than building a model of normal instances.

Isolation forest detects anomalies faster and requires less memory space compared to other anomaly detection algorithms.

To handle high-dimensional and large-sized input data

Inappropriate feature extraction & defining normal and abnormal behaviour in the data, variations in the abnormal data increase the dataset's complexity. In such cases, isolation forest cannot be used.

Data should contain only numeric/Continuous datatype variables. Data should not contain any missing values.

Any classification dataset with numeric input variables

• Anomaly scores
• Anomaly labels
• Score samples cluster plot containing inliers
• Outliers (anomalies).
• The input dataset is classified into two categories as 1 and -1.
  • -1 implies-Outliers.
  • 1 implies normal data points.

It works on the principle of the decision tree algorithm. It works on the principle of decision tree algorithms, but that cannot be defined in the statistical methods used section as a decision tree is an ML algorithm.

It fails to detect local anomaly points, which affects the accuracy of the algorithm.