Predicting the ideological direction of Supreme Court decisions: ensemble vs. unified case-based model
Code for the paper, "The Curse of Dimensionality: Inside Out", DOI = 10.13140/RG.2.2.29631.36006.
Code for animations used in the blog on The Curse of Dimensionality
CFOF developed in Python. Based on Angiulli's works : https://arxiv.org/pdf/1901.04992v2.pdf
Since the times of d'Alembert, Lagrange and Euler humans like to add fictitious dimensions to their real-world physical and mathematical problems. This art was perfected in the XX-th century by Heisenberg, Pauli and Dirac in their 'matrix mechanics'. In the XXI-st century we can contribute to this proud tradition too, we have computers! :)
To explore the curse of dimensionality and regularization with logistic regression and KNN models
Performing PCA(the unsupervised learning technique) for reducing the dimensions
Analyzing and overcoming the curse of dimensionality and exploring various gradient descent techniques with implementations in R
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