Gain access to our proprietary, best-in-class, tree-based machine learning algorithms, TreeNet and Random Forests, for the advanced predictive analytics power boost you need that easily complements your Minitab Statistical Software subscription.
What are tree-based methods? Tree-based algorithms utilize a series of if-then rules to create predictions from one or more decision trees. Compared to linear models like regression, tree-based methods map non-linear relationships quite well and can overcome the messiness in data that other methods simply cannot while also providing not only speed to answer, which can help you save time, but remarkable accuracy and ease of interpretation.
Our most flexible, award-winning and powerful machine learning tool, TreeNet Gradient Boosting, is known for its superb and consistent predictive accuracy due to its iterative structure that corrects combined errors of the ensemble as it builds.
Based on a collection of CART Trees, Random Forests leverages repetition, randomization, sampling, and ensemble learning in one convenient place that brings together independent trees and determines the overall prediction of the forest.
“I usually stick to the methods that have always worked for me—regression helps identify the x’s that drive the y’s. But the TreeNet partial dependency plots have given me deeper insight and have helped me solve some of my company’s most vexing problems. ”
- Process Engineer,
Consumer Packaged Goods
“Our Continuous Improvement teams are making great progress with Minitab’s Predictive Analytics. The integration of data science and continuous improvement has resulted in more predictable KPI’s. With the increased focus on data, analytics, and business performance management - Minitab Solutions help us put that all together! ”
- Data Science Leader, Food Manufacturer
“ Engineers and analysts can spend 80% of their time trying to identify the important drivers of process issues when performing a root cause analysis.”
- Minitab Research