AI & MACHINE LEARNING PROGRAM • LEVEL 15 — ANOMALY DETECTION

Measure an Isolation Split with Python

Learn measure an isolation split with python with a short, executable Python example.

IntermediateIsolation forestSplit

PROBLEM UNDERSTANDING

Input and expected output

Sample input

No input required

Sample output

[1, 2, 3] [100]

COMPLETE PYTHON PROGRAM

Complete Python implementation

values = [1,2,3,100]
threshold = 50
left = [x for x in values if x<threshold]
right = [x for x in values if x>=threshold]
print(left, right)

CURRENT STEP

Select Start to walk through the important lines.

SELECTED LINE

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EXPECTED OUTPUT FOR THE SAMPLE

[1, 2, 3] [100]

0%Step 0 of 0

PROGRAM EXPLANATION

Algorithm and explanation

Initialize the sample values used to measure an isolation split.
Process the data step by step using Isolation forest and Split.
Display the result for measure an isolation split and compare it with the documented sample output.

This example of measure an isolation split processes the sample values in a controlled iteration. It demonstrates Isolation forest and Split and prints a deterministic result that can be checked against the sample output.

EFFICIENCY

Time and space complexity

Time complexity

O(n)

Auxiliary space

O(n)

DEBUGGING CHECKLIST

Common mistakes

Check this

For measure an isolation split, keep the data shape and value types consistent with Isolation forest.

Check this

Keep every dependent statement inside the correct indented Python block.

Check this

Verify the final Isolation forest and Split result against the sample before trying new data.

Try it yourself

Practice: Run the program with the sample input, predict its output, and then test one boundary case of your own.