Features

These are just a few of the reasons you’ll want to try out Tribuo.

Provenance

Tribuo’s Models, Datasets and Evaluations have provenance, they know exactly what parameters, transformations and files were used to create them. This means each model can be rebuilt from scratch, and experiments are easy as the evaluation tracks the models and datasets used.

Type-safety

Tribuo is strongly typed (like Java). Each model knows what kind of output it produces, what inputs it expects and the names of everything involved. No more confusion when loading something off disk, Tribuo knows what kind of model it is and what labels it can predict.

Interoperability

Tribuo provides interfaces to popular ML libraries like XGBoost and Tensorflow, along with support for the onnx model exchange format. Our ONNX support (via onnx-runtime) allows you to deploy models built in other packages and other languages (such as Python’s scikit-learn) alongside models trained with Tribuo.

Algorithms

Tribuo offers support for many popular machine learning algorithm. These algorithms are grouped by type, and Tribuo’s abstract interface makes switching between implementations simple.

General predictors

Tribuo has several implementations which can be used for a variety prediction tasks:

Algorithm Implementation Notes
Bagging Tribuo Can use any Tribuo trainer as the base learner
Random Forest Tribuo Can use any Tribuo tree trainer as the base learner
K-NN Tribuo Has several parallel backends, as well as a single threaded backend
Neural Networks TensorFlow Via the TensorFlow interface. Models can be deployed using the ONNX interface or the TF interface

The ensembles and K-NN use a combination function to produce the output, those combiners are prediction task specific but the ensemble & K-NN implementations are task agnostic. We provide voting and averaging combiners for classification and regression tasks.

Classification

Tribuo has implementations or interfaces for:

Algorithm Implementation Notes  
Linear models Tribuo Uses SGD and allows any gradient optimizer  
CART Tribuo    
SVM-SGD Tribuo An implementation of the Pegasos algorithm  
Adaboost.SAMME Tribuo Can use any Tribuo classification trainer as the base learner  
Multinomial Naive Bayes Tribuo    
LIME Tribuo Our LIME implementation allows mixing of text and tabular data, but does not support images  
Regularised Linear Models LibLinear    
SVM LibSVM or LibLinear LibLinear only supports linear SVMs  
Gradient Boosted Decision Trees XGBoost    

Tribuo also has a linear chain CRF for sequence classification tasks. This is also trained via SGD using any of Tribuo’s gradient optimizers.

Regression

Tribuo’s regression algorithms are multidimensional by default, any single dimensional implementations are wrapped so they can produce a multidimensional output.

Algorithm Implementation Notes
Linear models Tribuo Uses SGD and allows any gradient optimizer
CART Tribuo  
Lasso Tribuo Using the LARS algorithm
Elastic Net Tribuo Using the co-ordinate descent algorithm
Regularised Linear Models LibLinear  
SVM LibSVM or LibLinear LibLinear only supports linear SVMs
Gradient Boosted Decision Trees XGBoost  

Clustering

Tribuo has infrastructure for clustering and a single algorithm. We expect to add new implementations over time.

Algorithm Implementation Notes
K-Means Tribuo Has both sequential and parallel backends

Anomaly Detection

Tribuo has infrastructure for anomaly detection tasks and a single backend implementation using LibSVM. We expect to add new implementations over time.

Algorithm Implementation Notes
One-class SVM LibSVM  

Interfaces

In addition to our own implementations of Machine Learning algorithms, Tribuo also provides a common interface to popular ML tools on the JVM. If you’re interested in contributing a new interface, open a GitHub Issue, and we can discuss how it would fit into Tribuo.

Currently we have interfaces to:

  • LibLinear - via the LibLinear-java port of the original LibLinear.
  • LibSVM - using the pure Java transformed version of the C++ implementation.
  • ONNX Runtime - via the Java API contributed by our group.
  • TensorFlow - Using the 1.14 Java API. We’re participating in the Tensorflow JVM SIG, and the upcoming TensorFlow 2 Java API will support training models without Python, which we’ll incorporate into Tribuo when it’s released.
  • XGBoost