MLBenchmarks.jl

This repo provides Julia based benchmarks for ML algo on tabular data. It was developed to support both NeuroTreeModels.jl and EvoTrees.jl projects.

For each dataset and algo, the following methodology is followed:

• Data is split in three parts: train, eval and test
• A random grid of 16 hyper-parameters is generated
• For each parameter configuration, a model is trained on train data until the evaluation metric tracked against the eval stops improving (early stopping)
• The trained model is evaluated against the test data
• The metric presented in below are the ones obtained on the test for the model that generated the best eval metric.

The following selection of common tabular datasets is covered:

• Year: min squared error regression
• MSRank: ranking problem with min squared error regression
• YahooRank: ranking problem with min squared error regression
• Higgs: 2-level classification with logistic regression
• Boston Housing: min squared error regression
• Titanic: 2-level classification with logistic regression

Comparison is performed against the following algos (implementation in link) considered as state of the art on tabular data problems tasks:

• EvoTrees
• XGBoost
• LightGBM
• CatBoost
• NODE

• Neural Oblivious Decision Ensembles for Deep Learning on Tabular Data.
  • https://github.com/Qwicen/node
  • https://github.com/manujosephv/pytorch_tabular
• Attention augmented differentiable forest for tabular data
• NCART: Neural Classification and Regression Tree for Tabular Data
• Squeeze-and-Excitation Networks
• Deep Neural Decision Trees
• XGBoost: A Scalable Tree Boosting System
• CatBoost: gradient boosting with categorical features support
• LightGBM: A Highly Efficient Gradient Boosting Decision Tree
• Deep Neural Decision Trees
• Neural Decision Trees