In this exploration of the data landscape, we embark on a journey through the staggering growth of global data generation. The sheer magnitude of information created in 2024, reaching a projected 140 zettabytes, underscores the exponential nature of this phenomenon.
Tag: Distance
Today in this document I will explain the concept of recall using a confusion matrix and outlines situations where recall should be prioritized, such as in healthcare diagnostics, fraud detection, and imbalanced datasets. I will also discuss instances where recall
Manhattan distance, a metric used to calculate distances in grid-like structures, is an important metric in distance calculation. In this document I will be sharing details around the same. I will also share real-world applications of Manhattan distance, including optimizing
New to data science? Cosine Similarity might sound complex, but this guide breaks it down in simple terms. Learn how this metric is used to measure similarity between text documents and its real-world applications. Things we will cover in this
This document that I share today, explores the concept of Euclidean distance and its application in measuring user similarity, particularly in the context of recommendation systems. I am sharing clear explanation of Euclidean distance, its calculation, and interpretation of results.