Linear regression plays a big part in the everyday life of a data analyst, but the results aren’t always satisfactory. What if you could drastically improve prediction accuracy in your regression with a new model that handles missing values, interactions, AND nonlinearities in your data? Instead of proceeding with a mediocre analysis, join us for this presentation, which will show you how MARS nonlinear regression, TreeNet gradient boosting, and Random Forests can take your regression model to the next level with modern algorithms designed to expertly handle your modeling woes. With these state-of-the-art techniques, you’ll boost model performance without stumbling over confusing coefficients or problematic p-values!

Linear regression plays a big part in the everyday life of a data analyst, but the results aren’t always satisfactory. What if you could drastically improve prediction accuracy in your regression with a new model that handles missing values, interactions, AND nonlinearities in your data?
As a follow-up to the first webinar, we show you how to take these techniques even further to extract actionable insight and take advantage of advanced modeling features. Walk away with several different methods to turn your ordinary regression into an extraordinary regression!

This webinar will show you how to optimize your targeted marketing using techniques common in analytics, data science, and machine learning. We will demonstrate with real-world data from a Portuguese Bank’s direct marketing campaign. You will be offered software, data, and step-by-step instructions so that you can replicate all steps after the webinar, both on provided data and even your own. Watch to understand how to implement data science techniques and why these techniques are important in targeted marketing.

Learn to control the size of the maximal CART tree in two ways: Telling CART to stop early and limiting CART's freedom to produce small nodes.

This series focuses on what Salford Systems calls “batteries,” which are pre-packaged scenarios that are inspired by how leading analysts structure their modeling work.

In this series we will show how easy it is to use the SPM software suite for your predictive modeling projects. We will use a modern banking application as an example.

An approach to data mining from a statistical point of view.

A case study example of using the SPM software for ecological nighe modeling.

In this on-demand webinar, we will show you how TreeNet Gradient Boosting can be used for the 2009 KDD Cup competition to quickly achieve a place in the top 5. At the end of this webinar, our goal is that you will be able to build a TreeNet Model that can bring you within decimal places of a winning solution. Use this information as a starting point for Kaggle competitions and other KDD Cup competitions.

This series is designed for the beginner or someone new to Salford Systems' data mining products. Start building models quickly and competently.

In this 10-part video series we discuss the concept of cost functions, which are directly related to the performance of data mining and predictive models.

Use Battery SHAVE in the Salford Predictive Modeler® software suite to improve your model performance, increase model simplicity, and decrease the number of predictors needed for an accurate model. Using this battery will hep streamline and automate your model for optimal results.

Return on investment is a profitability measure that many companies use to quantify their efforts and make important business decisions. As an example, we will look at ROI related to product sales and promotions at Walmart. Using state-of-the-art data science techniques, and especially TreeNet gradient boosting, we will optimize product promotion options and maximize revenue.

Return on investment is a profitability measure that many companies use to quantify their efforts and make important business decisions. As an example, we will look at ROI related to product sales and promotions at Walmart. Using state-of-the-art data science techniques, and especially TreeNet gradient boosting, we will optimize product promotion options and maximize revenue.

Learn to address the challenge of testing small training data sets and improve the reliability of results using Battery Cross-Validation (CVR).

Understand and apply the benefits of combining CART Classification and Regression Trees with tree ensemble methods like Random Forests and TreeNet stochastic gradient boosting.

Including OLS, nonlinear regression splines, Generalized PathSeeker and modern hybrid models.

Segmentation (targeting, profiling, classification) is the process of dividing a database into distinct groups of individuals who share common characteristics. This is readily accomplished using modern data mining and machine learning techniques. The methods are easily implemented and work well with large datasets containing nonlinearities, interactions in the data, and a mix of categorical and numerical variables.