The Pleasure of Finding Things Out

Using the Freedman-Diaconis Rule to determine optimal histogram bin width

Assessing goodness of fit using scipy and matplotlib

Updating sample mean and variance to account for new observations without full recalculation

Visualizing the fit of parametric distributions to empirical loss data using ggplot2

A walkthrough of the k-means clustering algorithm

Examination of the LOESS method with implementation in Python

Working with data.table’s special symbols

Derivation of the Poisson as a limiting case of the Binomial PDF

Exploring denisty estimation with various kernels in Python

Assessing the quality of machine learning classifiers with scikit-learn

Python implementation of Newton’s method of polynomial interpolation

A smoothed empirical percentile implementation in Python

Assessing the quality of fit of a Logistic Regression model

Estimating Logistic Regression coefficents in Python

Manual estimation of logistic regression coefficents in R

Derivation of the Normal Equations via Least Squares and Maximum Likelihood