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
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
CFOF developed in Python. Based on Angiulli's works : https://arxiv.org/pdf/1901.04992v2.pdf
Analyzing and overcoming the curse of dimensionality and exploring various gradient descent techniques with implementations in R
Performing PCA(the unsupervised learning technique) for reducing the dimensions
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