Скачать или смотреть Setting Up TSNE for Optimal Cluster Visualization

@genaiexp Setting up TSNE properly is crucial for achieving optimal cluster visualization. The first step involves preprocessing your data. This often includes normalizing or standardizing the data to ensure that all features contribute equally to the distance computations. Next, you'll need to configure several key parameters in TSNE. One of the most important is perplexity, which affects the balance between local and global aspects of your data. A rule of thumb is to set perplexity between 5 and 50, but the optimal value can depend on the size and nature of your dataset. Another critical parameter is the learning rate, which controls how TSNE updates its embedding. A learning rate that is too small may lead to slow convergence, while one that is too large can cause the algorithm to diverge. Finally, the initialization of TSNE is random, meaning that different runs can produce different results. It's often a good idea to run TSNE multiple times with different initializations to ensure stable results. By understanding and configuring these parameters properly, you can significantly enhance the quality of your cluster visualizations.