Announcing the GAUSS Machine Learning Library

by Eric · Published August 28, 2023

Introduction

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.

Machine Learning Models at Your Fingertips

With the GAUSS Machine Learning library, you can run machine learning models out of the box, even without any machine learning background. It supports fundamental machine learning models for classification and regression including:

• Logistic regression.
• LASSO and ridge regression.
• Decision forests.
• Principal component analysis.
• K-nearest neighbors.
• K-means clustering.

Quick and Painless Data Preparation and Management

We know model fitting and prediction is just the tip of the iceberg when it comes to any data analysis project. That's why we've focused on making GAUSS one of the best environments for data import, cleaning, and exploration.

GML provides machine learning specific data preparation tools including:

• One-hot encoding.
• Testing and training data splits.
• Cross-validation splits.
• Internal data scaling.

See how GAUSS reduces the pain and time of data wrangling and let's you get to the heart of your machine learning models quicker.

Easy to Implement Model Evaluation

Compare and evaluate machine learning models with tools for GML plotting and performance evaluation tools:

• Data class plots.
• Model mean squared error.
• Classification metrics.
• Variable importance tables.

Unparalleled Customer Support

We pride ourselves on offering unparalleled customer support and we truly care about your success. If you can't find what you need in our online documents, user forum, or blog, you can be confident that a GAUSS expert is here to quickly resolve your questions.

See It In Action

Want to see GML in action? Check out these real-world applications:

1. Classification With Regularized Logistic Regression.
2. Machine Learning With Real-World Data.
3. Understanding Cross-Validation.
4. Fundamentals of Tuning Machine Learning Hyperparameters.
5. Predicting The Output Gap With Machine Learning Regression Models.
6. Applications of Principal Components Analysis in Finance.
7. Predicting Recessions With Machine Learning Techniques.