The new GAUSS Machine Learning (GML) library offers powerful and efficient machine learning techniques in an accessible and friendly environment. Whether you’re just getting familiar with machine learning or an experienced technician, you’ll be running models in no time with GML.
The preliminary econometric package for Time Series and Panel Data Methods has been updated and functionality has been expanded with over 20 new functions in this release of TSPDLIB 3.0.0. The TSPDLIB 3.0.0 package includes expanded functions for time series and panel data testing both with and without structural breaks and causality testing. It requires a GAUSS 23+ for use.
Logistic regression has been a long-standing popular tool for modeling categorical outcomes. It’s widely used across fields like epidemiology, finance, and econometrics. In today’s blog we’ll look at the fundamentals of logistic regression. We’ll use a real-world survey data application and provide a step-by-step guide to implementing your own regularized logistic regression models using the GAUSS Machine Learning library, including:
- Data preparation.
- Model fitting.
- Classification predictions.
- Evaluating predictions and model fit.
If you’ve ever done empirical work, you know that real-world data rarely, if ever, arrives clean and ready for modeling. No data analysis project consists solely of fitting a model and making predictions. In today’s blog, we walk through a machine learning project from start to finish. We’ll give you a foundation for completing your own machine learning project in GAUSS, working through:
- Data Exploration and cleaning.
- Splitting data for training and testing.
- Model fitting and prediction.
If you’ve explored machine learning models, you’ve most likely encountered the term “cross-validation” at some point. Cross-validation is an important step for training robust and reliable maachine learning models. In this blog, we’ll break cross-validation into simple terms. Using a practical demonstration, we’ll equip you with the knowledge to confidently use cross-validation in your machine learning projects.
Machine learning algorithms often rely on hyperparameters that can impact the performance of the models. These hyperparameters are external to the data and are part of the modeling choices that practitioners must make. An important step in machine learning modeling is optimizing model hyperparameters to improve prediction accuracy. In today’s blog, we will cover some fundamentals of parameter tuning and will look more specifically at fine-tuning our previous decision forest model.
In today’s blog, we compare three different machine learning regression techniques for predicting U.S. real GDP output gap. We will use a combination of common economic indicators and GDP subcomponents to predict the quarterly GDP output gap.
Working with strings hasn’t always been easy in GAUSS. In the past, the only option in GAUSS was to store strings separately from numeric data. It made it difficult to work with datasets that contained mixed types. With the introduction of GAUSS dataframes in GAUSS 21 and the enhanced string capabilities of GAUSS 23, that has all changed! I would argue that GAUSS now offers one of the best environments for managing and cleaning mixed-type data. I recently used GAUSS to perform the very practical task of creating an email list from a string-heavy dataset – something I never would have chosen GAUSS for in the past. In this blog, we walk through this data cleaning task, highlighting several key features for handling strings.
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.