ML + XAI -> Strong GLM in Python
In this post, we use Python to improve a simple GLM by insights from a boosted trees model.
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Generating Correlated Random Samples in Python
An analysis may require the ability to generate correlated random samples. For example, imagine we have monthly returns for three financial indicators over a 20 year period. We are interested in modeling these returns using parametric distributi...
Bessel’s Correction
Bessel’s correction is the use of instead of in the sample variance formula where is the number of observations in a sample. This method corrects the bias in the estimation of the population variance.
Recall that bias is defined as: where r...
Denoising Signals using the FFT
The Discrete Fourier Transform (DFT) turns a data vector into a sum of sine/cosine components. The DFT is a Fourier series on data instead of analytic functions. Why do we perform the DFT? Because the features typically of interest aren’t always...
GeoHashing from Scratch in Python
I recently became interested in GeoHashing, and wanted to develop an understanding of the algorithm with the goal of implementing it myself. I was surprised to find it to be quite simple and intuitive. In what follows, I’ll demonstrate how to ge...
Gradient Descent for Logistic Regression
Within the GLM framework, model coefficients are estimated using iterative reweighted least squares (IRLS), sometimes referred to as Fisher Scoring. This works well, but becomes inefficient as the size of the dataset increases: IRLS relies on the...
Querying SQL Server with Pandas
Pandas is a powerful, flexible and easy to use open source data analysis and manipulation tool built on top of the Python programming language. It has become the data manipulation library of choice for Machine Learning and Data Science practition... [...Read more...]
A Matrix Factorization Approach to Linear Regression
This post is intended to shed light on why the closed form solution to linear regression estimates is avoided in statistical software packages. But we start by first by deriving the solution to the normal equations within the standard multivariat...
Generating Correlated Random Samples in Python
An analysis may require the ability to generate correlated random samples. For example, imagine we have monthly returns for three financial indicators over a 20 year period. We are interested in modeling these returns using parametric distributi...
Generating Correlated Random Samples in Python
An analysis may require the ability to generate correlated random samples. For example, imagine we have monthly returns for three financial indicators over a 20 year period. We are interested in modeling these returns using parametric distributio...
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