Tree-Based Models

What are Tree-Based Models?

Tree-based models use a decision tree to represent how different input variables can be used to predict a target value. Machine learning uses tree-based models for both classification and regression problems, such as the type of animal or value of a home. The input variables are repeatedly segmented into subsets to build the decision tree, and each branch is tested for prediction accuracy and evaluated for efficiency and effectiveness. Splitting the variables in a different order can reduce the number of layers and calculations required to produce an accurate prediction. Generating a successful decision tree results in the most important variables (most influential on the prediction) being at the top of the tree hierarchy, while irrelevant features get dropped from the hierarchy.


Why are Tree-Based Models Important?

Tree-based models are a popular approach in machine learning because of a number of benefits. Decision trees are easy to understand and interpret, and outcomes can be easily explained. They accommodate both categorical and numerical data and can be used for both classification and regression models. Computationally, they perform well even for large data sets and require less data preparation than other techniques.


How C3 AI Enables Organizations to Use Tree-Based Models

C3 AI provides leading enterprise AI technology that enables large organizations to harness and extract value using a wide variety of AI and machine learning libraries, resulting in step-function improvements across business processes. The C3 AI® Platform is a complete, end-to-end platform for designing, developing, deploying, and operating enterprise AI applications at industrial scale. The C3 AI Platform provides comprehensive capabilities enabling organizations to prepare unlimited volumes of data, and then apply advanced AI and machine learning algorithms to generate predictions and insights to drive the business. The C3 AI Platform supports a wide range of third-party and open-source libraries and frameworks for advanced mathematical, statistical and machine learning capabilities, including Spark MLib, TensorFlow, Keras, Scikit-learn, cuDNN, NumPy, SciPy, Caffe, Torch, PyTorch, Lex, Polly, Rekognition, Azure ML,, Stanford Core NLP, NLTK, spaCY, fbProphet, StatsModels, and XGBoost.