# t-SNE dimensionality reduction
URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/model-eval-metrics/dim-reduction.html

> Review examples for taking a DataRobot project and exporting its model insights as both machine readable files and plots in various file formats.

# t-SNE dimensionality reduction

[Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/advanced_ml_and_api_approaches/Dimensionality%20reduction%20in%20DataRobot%20with%20t-SNE/Dimensionality%20reduction%20in%20DataRobot%20with%20t-SNE.ipynb)

This accelerator provides examples for taking a DataRobot project and exporting its model insights as both machine readable files and plots in various file formats using t-Distributed Stochastic Neighbor Embedding (t-SNE). t-SNE is a powerful technique for dimensionality reduction that can effectively visualize high-dimensional data in a lower-dimensional space. Dimensionality reduction can improve machine learning results by reducing computational complexity of the algorithms, preventing overfitting, and focusing on the most relevant features in the dataset. Note that this technique should only be used when the number of features is low.

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