A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
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H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
- bayesian-optimization
- bayesian-optimisation
- hyperparameter-optimization
- hyperparameter-tuning
A Fast Implementation of Random Forests
ThunderGBM: Fast GBDTs and Random Forests on GPUs
Little Ball of Fur - A graph sampling extension library for NetworKit and NetworkX (CIKM 2020)
- graph
- graph-sampling
- network-sampling
- random-walk
A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models in Keras.
- python
- machine-learning
- random-forest
- tensorflow
Selected Machine Learning algorithms for natural language processing and semantic analysis in Golang
- go
- golang
- natural-language-processing
- nlp
Hyperparameter optimization and feature selection for scikit-learn using evolutionary algorithms. A modern alternative to GridSearchCV and RandomizedSearchCV.
The official implementation of "The Shapley Value of Classifiers in Ensemble Games" (CIKM 2021).
- shapley
- shapley-value
- owen
- voting-classifier
BatchFlow helps you conveniently work with random or sequential batches of your data and define data processing and machine learning workflows even for datasets that do not fit into memory.
- python3
- data-science
- machine-learning
- python
Go Scoring API for PMML
- golang
- pmml
- random-forest
- classification
Ruby Scoring API for PMML
- ruby-gem
- pmml
- random-forest
- classification
Single Image Random Dot Stereogram for Tensorflow
- tensorflow
- jupyter-notebook
- 3d-reconstruction
- stereogram-creator
Comparing the performance of Stacked Ensemble Learning & machine learning algorithms like Random Forest, Decision Tree, Adaboost, Gradient Boost and XGBoost Classifier in Python for Stock Market Trend Prediction.