Jaccard Index for Binary Classification

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: 1. Calculate the Jaccard Index between the arrays y true and y pred. 2. Return the Jaccard Index as a float value. 3. 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).