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Vertica Blog

Machine Learning

Make data analysis easier with dimensionality reduction

This blog post was authored by Anh Le. Introduction As the number of features in your data set grows, it becomes harder to work with. Visualizing 2D or 3D data is straightforward, but for higher dimensions you can only select a subset of two or three features to plot at a time, or turn to […]

Machine Learning Key Terms

This blog post was authored by Soniya Shah. Machine learning seems to be everywhere these days – in the online recommendations you get on Netflix, the self-driving cars that hyped in the media, and in serious cases, like fraud detection. Data is a huge part of machine learning, and so are the key terms. Unless […]

What’s New in Vertica 9.1: Precision-Recall Curve and F1-Score Machine Learning Evaluation Functions

This blog post was authored by Ginger Ni. The precision-recall curve is a measure for evaluating binary classifiers. It is a basic measure derived from the confusion matrix. In Vertica 9.1, we provide a new machine learning evaluation function PRC() for calculating precision and recall values from the results of binary classifiers. Along with the […]

Using Vertica Machine Learning to Analyze Smart Meter Data

This blog post was authored by Soniya Shah. Machine learning and data science have the potential to transform businesses because of their ability to deliver non-obvious, valuable insights from massive amounts of data. However, many data scientist’s workflows are hindered by computational constraints, especially when working with very large data sets. While most real-world data […]

What’s New in Vertica 9.0.1: Machine Learning

This blog post was authored by Soniya Shah. Vertica 9.0.1 introduces new functionality that continues to match our goals for fast-paced development and enhancement of machine learning in Vertica. In this release, we introduce support for random forest for regression, a new statistical summary function, increased support for cross validation, and enhancements for data evaluation. […]

Estimate the Price of Diamonds Using Vertica Machine Learning

This blog post was authored by Vincent Xu. In this blog post, I’ll take you through the exercise I did to estimate the price of a diamond based on its characteristics, using the linear regression algorithm in Vertica. Besides Vertica 9.0, I used Tableau for charting and DbVisualizer as the SQL editor. From this exercise, […]

Machine Learning Mondays: Vertica 9.0 Cheat Sheet

This blog post was authored by Vincent Xu. Vertica 9.0 is out and here is the updated Vertica machine learning cheat sheet. Vertica 9.0 introduces a slew of new machine learning features including one-hot encoding, Lasso regression, cross validation, model import/export, and many more. See the cheat sheet for examples of how to use the […]

What’s New in Vertica 9.0: Machine Learning Enhancements

This blog post was authored by Soniya Shah. Vertica 9.0 introduces new functionality that continues to match our goals for fast-paced development of the existing machine learning functions. In this release, we introduce two new summary functions, support for cross validation, support for one hot encoding, and the ability to import and export your models […]

Compute Engine or Analytical Data Mart for Distributed Machine Learning? Vertica Explains How to Choose

This blog post was authored by Sarah Lemaire. On Tuesday, August 22, The Boston Vertica User Group hosted a late-summer Meetup to talk to attendees about compute engines and data mart applications, and the advantages and disadvantages of both solutions. In the cozy rustic-industrial atmosphere of Commonwealth Market and Restaurant, decorated with recycled wood pallets, […]

What’s New in Vertica 8.1.1: Machine Learning

This blog post was authored by Soniya Shah. Vertica 8.1.1 continues with the fast-paced development for machine learning. In this release, we introduce the highly-requested random forest algorithm. We added support for SVM to include SVM for regression, in addition to the existing SVM for classification algorithm. L2 regularization was added to both the linear […]