Keras is an API used for running high-level neural networks. The model runs on top of TensorFlow, and was developed by Google.
The main competitor to Keras at this point in time is PyTorch, developed by Facebook. While PyTorch has a somewhat higher level of community support, it is a particularly verbose language and I personally prefer Keras for greater simplicity and ease of use in building and deploying models.
Continue reading “Keras: Regression-based neural networks”
Decision trees are a highly useful visual aid in analysing a series of predicted outcomes for a particular model. As such, it is often used as a supplement (or even alternative to) regression analysis in determining how a series of explanatory variables will impact the dependent variable.
Continue reading “Decision Trees and Random Forests in R”
Serial correlation (also known as autocorrelation) is a violation of the Ordinary Least Squares assumption that all observations of the error term in a dataset are uncorrelated. In a model with serial correlation, the current value of the error term is a function of the one immediately previous to it:
et = ρe(t-1) + ut
where e = error term of equation in question; ρ = first-order autocorrelation coefficient; u = classical (not serially correlated error term)
Continue reading “Serial Correlation: Durbin-Watson and Cochrane-Orcutt Remedy”