klotz: pca*

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  1. I used a Python t-SNE library to reduce the 200 feature dimensions for each word to 2 dimensions and plotted them in matplotlib. I saved out the x/y coordinates for each word in the book, so that I can show those words on the graph as you mouse over the replaced (blue) words.
  2. Discussion on the efficiency of Random Forest algorithms for PCA and Feature Importance. By Christopher Karg for Towards Data Science.

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