Minitab’s most flexible, award-winning and powerful machine learning tool, TreeNet® Gradient Boosting, is capable of consistently generating extremely accurate models.
For those new to TreeNet, it is a powerful implementation of the modern machine learning class of algorithms generally known as Stochastic Gradient Boosting. Developed by Jerome Friedman at Stanford University, the technique is known for its superb predictive accuracy. The secret is in the way a model is built: at each iteration a small tree is added to the current ensemble of trees to correct the combined errors of the ensemble.
Utilizing the variety of the supplied loss functions, the process can be tuned for the specific predictive modeling task, like least squares regression, robust regression, classification, etc. To assist with the model interpretation, TreeNet goes one step further and automatically generates various 2D and 3D plots to explain the nature of dependency of the response variable on the model inputs. The model is flexible enough to automatically discover and incorporate various non-linearities and multi-way interactions. A further set of controls allows the user to fine-tune model interactions to meet specific design objectives.
Interaction detection within our TreeNet modeling engine establishes whether interactions of any kind are needed in a predictive model. This system not only helps improve model performance, often dramatically, but also assists in the discovery and use of valuable new insights.
Whether you're just getting started or looking to take your predictive analytics capabilities to the next level, Minitab's tree-based modeling engines have the power you need.
The ultimate classification tree algorithm that revolutionized advanced analytics and inaugurated the current era of data science.
The power to leverage multiple alternative analyses, randomization strategies, and ensemble learning in one convenient place.
The most flexible and powerful machine learning tool that is capable of consistently generating extremely accurate models.