Choosing the Right Distance Metric
Автор: NextGen AI Explorer
Загружено: 2025-06-03
Просмотров: 30
Описание: @genaiexp The choice of distance metric plays a crucial role in the performance of TSNE. It determines how the algorithm perceives the similarity between data points. The most common metric used in TSNE is the Euclidean distance, which works well for many types of data. However, depending on the nature of your dataset, other metrics such as Manhattan, cosine, or correlation distance might be more appropriate. For instance, cosine distance is often used in text data where the angle between vectors is more meaningful than their magnitude. When selecting a distance metric, consider the characteristics of your data and what you want to achieve with your visualization. The distance metric affects how clusters are formed and can significantly impact the insights you derive from your data. It's beneficial to experiment with different metrics to see which one yields the most meaningful visualization for your specific dataset. Understanding the implications of your choice can lead to more accurate and insightful visualizations.
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