The Dice Coefficient is a statistical measure used to gauge the similarity between two sets. It is particularly useful in fields like computer vision and natural language processing for comparing the similarity of two samples. The coefficient ranges from 0 to 1, where 0 indicates no similarity and 1 indicates perfect similarity. It is calculated using the formula: \textDice = \frac2 |A \cap B||A| + |B| , where A and B are the two sets being compared. This measure is often preferred over other similarity metrics, such as Jaccard Index, because it gives more weight to the common elements between the sets. The Dice Coefficient is especially effective in applications like image segmentation, where it helps assess how well a predicted segmentation matches the ground truth. By providing a clear numerical value, it aids in evaluating the performance of algorithms and models.