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Task: Implement the Jaccard Index

Your task is to implement a function jaccard_index(y_true, y_pred) that calculates the Jaccard Index, a measure of similarity between two binary sets. The Jaccard Index is widely used in binary classification tasks to evaluate the overlap between predicted and true labels.

Your Task:

Implement the function jaccard_index(y_true, y_pred) to:

Calculate the Jaccard Index between the arrays y_true and y_pred.
Return the Jaccard Index as a float value.
Ensure the function handles cases where:
- There is no overlap between y_true and y_pred.
- Both arrays contain only zeros (edge cases).

The Jaccard Index is defined as:

\scriptsize \text{Jaccard Index} = \frac{\text{Number of elements in the intersection of } y_{\text{true}} \text{ and } y_{\text{pred}}}{\text{Number of elements in the union of } y_{\text{true}} \text{ and } y_{\text{pred}}}

Where:

$y_{\text{true}}$ and $y_{\text{pred}}$ are binary arrays of the same length, representing true and predicted labels.
The result ranges from 0 (no overlap) to 1 (perfect overlap).

Examples

Example 1:

Input:

y_true = np.array([1, 0, 1, 1, 0, 1])
y_pred = np.array([1, 0, 1, 0, 0, 1])
print(jaccard_index(y_true, y_pred))

Output: 0.75

Explanation: The Jaccard Index is calculated as 3 / 4 = 0.75, indicating a 75% overlap between the true and predicted labels.

Starter Code


import numpy as np

def jaccard_index(y_true, y_pred):
	# Write your code here
	return round(result, 3)

Calculate Jaccard Index for Binary Classification

Task: Implement the Jaccard Index

Your Task:

Examples

Starter Code