Category: Machine learning

Principal components analysis (PCA) is a useful tool that can help practitioners streamline data without losing information. In today’s blog, we’ll examine the use of principal components analysis in finance using an empirical example. We’ll look more closely at:

• What PCA is.
• How PCA works.
• How to use the GAUSS Machine Learning library to perform PCA.
• How to interpret PCA results.

Forecasts have become a valuable commodity in today’s data-driven world. Unfortunately, not all forecasting models are of equal caliber, and incorrect predictions can lead to costly decisions. Today we will compare the performance of several prediction models used to predict recessions. In particular, we’ll look at how a traditional baseline econometric model compares to machine learning models. Our models will include: * A baseline probit model. * K-nearest neighbors. * Decision forests. * Ridge classification.