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TreeNet Testimonials

Broadband propensity project: comparing TreeNet with Enterprise Miner using logistic regression.

We’re seeing these benefits
1. TreeNet (Stochastic gradient boosting) method injects randomization to the selection of candidate predictors and training data, making this method much more robust than the traditional statistical models especially in dealing with messy data. For example, in our dataset, there is a part of information missing like customer’s portfolio and usage data. Although we do the data replacement for the statistical models, it would still affect the final results as it uses the whole dataset for training. By using TreeNet, only a subset of data and predictors are used each time and this process will be repeated for hundreds of time. This method greatly reduced the influence of messy data and improves the robustness of the final model. In terms of the nature of modeling, growing a large number of small trees instead of using a single complex tree has been proved to be more accurate and robust.
Our modeling dataset is always of big size. In this example, data size is above 500,000 and the initial predictor set is about 160 predictors. TreeNet is computational efficient and scalable for the large dataset which is much faster than the enterprise miner. In the result analysis, the detailed relationships between predictors and the target are much easier to be visualized. Battery automates the process of running multiple experiments which reduces a lot of efforts in the predictor selection. In this example, 16 predictors are finally selected after 5 cycles and 2 battery processes.
2. Insights: TreeNet can dig out very granular information. For example, it helps to find the impact of a specific sector of predictors. In this example, predictors regarding to product holding and usage are emphasized in the TreeNet model while they are not prominent in the traditional logistic regression model. Information in these sectors contributed a lot in improving the prediction of customer’s propensity in buying our fix broadband products.
3. We’re seeing various levels of performance gains over traditional statistical models. For the best performance we’ve seen, the improvement of Lift is consistently about 40%, helping to capture more than 30% customers who are willing to buy our product.

Predictive Analytics Manager at Leading Telco in Singapore.
**Her team works on scientific marketing initiatives using statistics, data science and optimization methodologies.

David Vogel, CEO Voloridge Investment Management and Captain of the winning Heritage Health prize team

I have multiple versions of gradient boosting I could find including popular open source versions and TreeNet outperforms them all in predictive accuracy (consistently across many different kinds of data sets) while maintaining the ability to train models quickly.

David Vogel, CEO Voloridge Investment Management and Captain of the winning Heritage Health prize team
Florida, USA


Brad Turner, Vice President of Marketing and Business Development, Inkiru

Everyday, the Inkiru product predicts sales for 2000 items in an e-commerce context. In addition, the product generates a customized confidence interval for each prediction. The input is dynamic and it consists of 1 year of historical data. Each record contains approximately 150 features with information about sales, products, customers, and promotions.

The problem was very challenging from a modeling point of view. Important parts of the data were continuous, categorical, highly non-linear, sparse, missing, and noisy. We found Salford Systems adequate to deal with these characteristics of the data.

Precision was an important goal in this project. A validation with real data reports 90% of the predictions lying within 7 units of the actual sales and 50% within 2 units. Salford Systems was definitely an important tool to reach this degree of accuracy in the product.

 Brad Turner, Vice President of Marketing and Business Development, Inkiru
California, USA

Andrew Russo, Vice President, Modeling and Analytics at AccuData Integrated

As a traditional modeler, I had been primarily using regression and logistic regressions. I began to test TreeNet last fall. Since then I have built several models that are now market-tested and are performing as predicted by TreeNet. The real value of TreeNet has been the speed in which it builds data, the accuracy of its predictions and the incremental lift it is experiencing in side-by-side tests of regressions. It has also proven to be a tremendous data prep time saver in its ability to deal with outliers, missing data as well as doing a decent job distinguishing between scale and categorical data. Importantly, the ability for less-hands-on model builds has enabled us to offer new modeling products to our clients that otherwise would not have had the budget to do a modeling project. In short this new, advanced capability is giving my company a competitive advantage.

 Andrew Russo, Vice President, Modeling and Analytics at AccuData Integrated Marketing
Florida, USA

Tom Osborn, Adjunct Professor at University of Technology

I've used TreeNet on commercial projects since '04. For customer and prospect targeting, it outperforms logistic family regression, neural nets and other methods in my kitbag. Key strengths: handling of missing values, robustness, general non-linearity, variable interactions. Clients like feedback on variable importance (more general than Shapley or PMVD). They also like seeing how the variable contribute to predictions. Fast and easy to use. Best - is developed on Jerry Friedman's great maths.

 Tom Osborn, Adjunct Professor (analytics/data mining) at University of Technology, Sydney
Sydney, Australia

Fred Hazelton, Master Statistician

Predicting Crowds at Walt Disney World Theme Parks
Since 1986, the Unofficial Guide to Walt Disney World has been helping visitors to Orlando’s theme parks get the most out of their time and money. Market research shows that the two most important factors that affect a visitors satisfaction with a Disney trip is; 1) how long did I have to wait in line and 2) how much did I get to see. The Unofficial Guide and its website, TouringPlans.com has become the best source for solving these two problems.
The most effective way to reduce the amount of time you wait in line and to increase the number of attractions you get to experience is to visit at a time of year when the crowds are lower and to use an optimal, computer designed touring plan. Touring Plans are great! They tell you the optimal order in which to experience the attractions with minimal wait, a classic implementation of the travelling-salesman problem. However, an optimal touring plan requires that we can predict with reasonable accuracy, the wait time at an attraction at any given time or day, for any given day of the year.
We at TouringPlans.com have been using traditional linear regression methods to predict wait times for several years. But, the limitations of regression are more apparent as we gather more and more data. Subscribers to our mobile application “Lines” can see our estimates for wait times and submit updates when they are in the park. The sporadic nature of the wait times that we gather make it difficult to utilize in a traditional regression environment. The ups and downs of wait times throughout the day are difficult to model using regression but perfect for a data mining tool.

Using Treenet
Some auxiliary variables such as Park Hours, Parade Schedules, Historic Wait Times and School Schedules are available for each wait time record in advance. These can be used in a traditional regression model to analyse the past and predict the future. But the true value of the data we gather is in its dynamic nature. Variables like current weather, attraction status (open or broken down), recent wait times and recent wait times for other attractions have a great impact on how long you will wait in line. These variables are not available for predictions in advance and the value of these variables is not available for all records in the database. For example, not every wait time record in the database will have a recent wait time submission. Treenet can easily handle missing data, whereas regression cannot.
In a traditional regression model, the burden of determining variable interactions is placed on the statistician, usually to be discovered using trial and error. It is easy to rationalize that wait time data must have plenty of interactions that have a great impact. Relationships between wait times at other attractions, relationships between park hours and parade schedules, etc. With dozens of variables, the process of identifying interactions (and transformations) is prohibitive in a traditional regression environment. In Treenet, the search for interactions and transformations is inherent, exhaustive and automatic – a refreshing saver of time and energy, allowing more resources for other tasks.

Fred Hazelton
Master Statistician

Constance Jiang, Data Analyst, Tencent, Inc.

As a Data Analyst in risk management fields, it is significant to distinguish quality consumers, so as to recognize and limit low ROI transactions. We use TreeNet to build classification models, work on regressive problems. This software not only provides us great choices of powerful algorithms for model training, but also shows its outstanding accuracy (10% better under same circumstances), ability to process huge datasets, like, over 100,000 records with 50 complicated variables. TreeNet is also highly productive and user-friendly, several minutes are quite enough for model training. Now we can now spend more time on the results analysis and decision making.
TreeNet's performance is impressive, satisfying, and could really adapted into real scenarios and reducing the related risks.

 Constance JiangData Analyst Tencent, Inc

Xu Jie from Nanjing University of Information Science & Technology

I am conducting a project about GIS, in which many data analysis are needed. Lacking useful tools, our project made slow progress. In an accidental chance I got a TreeNet trial version and it shocked me with its powerful capabilities of data analyzing, friendly user interface and most important of all, accuracy. After using it we got many benefits from it during our research and our project had gone much faster.
In many features of TreeNet, we like most is plots which offers graphs displaying after building model. This feature is especially useful to us which provides the most visual and easy way to find shortcomings and make improvement of the model. We like the multiple model setting up ways as well, it’s flexible and covers most aspects of our research.
What attracts me most is the amazing speed of TreeNet. We have used some other software before using TreeNet. None of them could build a model in such a short time. What’s more, using TreeNet, the painstaking procedures of data preprocessing are saved, it greatly accelerated our research. Since our data contains over 100 million of variables, using common software, it takes weeks to get a result of analyze. However, by using TreeNet, it takes just a couple of days.
In addition, the most important is the accuracy. Due to the defects of sampling stage, there are some noisy variables in our data, which brings instability to our model. By using TreeNet, we got more stable model of our research than by other tools. What’s more, the model we built by TreeNet could be repeated and verified.
As to ROI, I cannot say how much money we have saved by using this tool, but we did consider to buy a high performance computer(about 5000 US dollars)to assist our research. After using the software, we decided to postpone that purchasing.
We are getting to use TreeNet just a few months , but we really impressed by its powerfulness. We know we are using just a few common features, a lot of powerful features are still waiting for us to learn.

 Xu Jie
Nanjing University of Information Science & Technology


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