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Analytics

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 […]

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 […]

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 […]

Analytic Queries in Vertica

This blog post was authored by Soniya Shah. Analytic functions handle complex analysis and reporting tasks. Here are some example use cases for Vertica analytic functions: • Rank the longest standing customers in a particular state • Calculate the moving average of retail volume over a specific time • Find the highest score among all […]

Time Series Analytics

This blog post was authored by Soniya Shah. Time series analytics is a powerful Vertica tool that evaluates the values of a given set of variables over time and groups those values into a window based on a time interval for analysis and aggregation. Time series analytics is useful when you want to analyze discrete […]

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, […]

Geospatial Analysis on Shapefile of Longitude and Latitude Data Using Vertica: Hurricane Bonnie

This blog post was authored by Ginger Ni. Like any natural disaster, hurricanes can leave behind extensive damage to life and property. The question asked by NGOs, government agencies, and insurance companies is, “How can we predict the locations where a storm will inflict the most damage?” Modern spatial analysis enables us to predict the […]

Vertica In-Database Approximate Count Distinct Functions Using LogLogBeta

This blog post was authored by Ginger Ni. Counting Distinct Values Data cardinality is a commonly used statistic in data analysis. Vertica has the exact COUNT(DISTINCT) function to count distinct values in a data set, but the function does not scale well for extremely large data sets. When exploring large data sets, speed is critical. […]

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 […]