Linear Discriminate Analysis

(ns index)

1 Assignment 2: Linear Discriminant Analysis (LDA)

Instructions:

• Generate a suitable dataset that contains three classes.
• Scale, normalize, and/or encode your data appropriately.
• Implement and apply Univariate LDA.
• Make sure to use 5-fold cross-validation.
• Modify the “README.md” file to include the following sections
      * Summary: A one-paragraph summary of the algorithm that was implemented including any pertinent or useful information. If mathematics are appropriate, include those as well.
      * Reflection: One paragraph describing useful takeaways from the week, things that surprised you, or things that caused you inordinate difficulty.
• Make sure that your README file is formatted properly and is visually appealing. It should be free of grammatical errors, punctuation errors, capitalization issues, etc.

What I did:

• Generated dataset based on three groupings with overlaps.
• Visualized data with histograms and scatter plot.
• Implemented a univariate LDA from scratch.
• Implemented a multivariate LDA with Clojure’s built-in functionality (still tuning my Clojure workflow). Resampled with bootstrapping repeated b = 30 times.
       * Tested a scale and normalized model pipeline versus raw data. Both models performed equivalently.
• R interop to visualize the multivariate model with ggplots2.
• Rendered document with Clay, pushed to GitHub, and had GitHub Actions deploy the static rendering to a webpage.

source: src/index.clj