Skip to content
#

ensemble-learning

Star

Here are 556 public repositories matching this topic...

Language: All
Filter by language
All 556 Jupyter Notebook 254 Python 202 R 29 MATLAB 17 HTML 16 Java 4 C++ 3 Julia 3 TeX 2 C 1
Sort: Best match
Sort options
Best match Most stars Fewest stars Most forks Fewest forks Recently updated Least recently updated

h2oai / h2o-3

Star 5.7k

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.

python java data-science machine-learning multi-threading opensource r big-data spark deep-learning hadoop random-forest gpu naive-bayes h2o distributed pca gbm ensemble-learning automl
  • Updated Jan 2, 2022
  • Jupyter Notebook

awslabs / autogluon

Star 4k
Open

Feature Tools integration

1
willsmithorg
willsmithorg commented Dec 26, 2021

Could FeatureTools be implemented as an automated preprocessor to Autogluon, adding the ability to handle multi-entity problems (i.e. Data split across multiple normalised database tables)? So if you supply Autogluon with a list of Dataframes instead of a single Dataframe it would first invoke FeatureTools:

  • take the multiple Dataframes (entities) and try to auto-infer the relationship betwee
Read more
enhancement help wanted good first issue tabular
Find more good first issues
awesome-decision-tree-papers

benedekrozemberczki / awesome-decision-tree-papers

Sponsor
Star 1.9k

A collection of research papers on decision, classification and regression trees with implementations.

classifier machine-learning random-forest statistical-learning xgboost lightgbm gradient-boosting-machine ensemble-learning cart decision-tree tree-ensemble decision-tree-classifier gradient-boosting classification-trees decision-tree-learning decision-tree-model classification-model catboost regression-tree machine-learning-research
  • Updated Nov 27, 2021
  • Python

shubhomoydas / ad_examples

Star 731

A collection of anomaly detection methods (iid/point-based, graph and time series) including active learning for anomaly detection/discovery, bayesian rule-mining, description for diversity/explanation/interpretability. Analysis of incorporating label feedback with ensemble and tree-based detectors. Includes adversarial attacks with Graph Convolutional Network.

streaming timeseries time-series lstm generative-adversarial-network gan rnn autoencoder ensemble-learning trees active-learning concept-drift graph-convolutional-networks interpretability anomaly-detection adversarial-attacks explaination anogan unsuperivsed nettack
  • Updated Aug 25, 2021
  • Python

LAMDA-NJU / Deep-Forest

Star 697
Open

Better Documentation

4
xuyxu
xuyxu commented Feb 12, 2021

Thanks to the contributors, many new features have been developed. As a result, the current version of documentation could be ambiguous, and requires more explanation or demonstration.

This issue collects suggestions on the documentation. Any one is welcomed to improve the readability of the documentation. For contributors unfamiliar with our workflow on building the documentation, please refe

Read more
good first issue

Improve this page

Add a description, image, and links to the ensemble-learning topic page so that developers can more easily learn about it.

Curate this topic

Add this topic to your repo

To associate your repository with the ensemble-learning topic, visit your repo's landing page and select "manage topics."

Learn more