Episode 25: Machine Learning vs. Magic

In this episode, I found that Bambi (Bayesian Model Building Interface) is more than a little deer, rooted for the round-robin operator for genetic algorithms, and had my convictions that machine learning is not magic reinforced. References: HighFrequencyCovariance:  A Julia Package for Estimating Covariance Matrices Using High Frequency Financial Data Bambi: A Simple Interface for Fitting Bayesian Linear Models in Python Spbsampling: An R Package for Spatially Balanced Sampling plot3logit: Ternary Plots for Interpreting Trinomial Regression Models Learning Base R (2nd Edition) Python and R for the Modern Data Scientist Genetic algorithm with a new round-robin based tournament selection: Statistical properties analysis Healthcare researchers must be wary of misusing AI New method to identify symmetries in data using Bayesian statistics Python 3.11.0rc2 is now available Complex Layouts using the SG Procedures Factor Analysis Guide with an Example How to Choose Appropriate Clustering Method for Your Dataset How to Apply AI to Small Data Sets? The R Consortium Needs Your Help with satRdays Visualizing OLS Linear Regression Assumptions in R R-packages: TPCselect:  Variable Selection via Threshold Partial Correlation historicalborrow:  Non-Longitudinal Bayesian Historical Borrowing Models historicalborrowlong:  Longitudinal Bayesian Historical Borrowing Models kgp:  1000 Genomes Project Metadata DBIsqldf:  Manipulate R Data Frames Using SQL latentFactoR:  Data Simulation Based on Latent Factors TSdeeplearning:  Deep Learning Model for Time Series Forecasting