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h2oai / h2o-3

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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.

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 Apr 24, 2021
  • Jupyter Notebook

awslabs / autogluon

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BrokenPipeError: [Errno 32] Broken pipe

3
cpoptic
cpoptic commented Mar 31, 2021

When running TabularPredictor.fit(), I encounter a BrokenPipeError for some reason.
What is causing this?
Could it be due to OOM error?

Fitting model: XGBoost ...
-34.1179 = Validation root_mean_squared_error score
10.58s = Training runtime
0.03s = Validation runtime
Fitting model: NeuralNetMXNet ...
-34.2849 = Validation root_mean_squared_error score
43.63s =

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Windows OS bug good first issue help wanted
awesome-decision-tree-papers

benedekrozemberczki / awesome-decision-tree-papers

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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 Feb 11, 2021
  • Python

shubhomoydas / ad_examples

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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 Sep 25, 2020
  • Python

LAMDA-NJU / Deep-Forest

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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

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good first issue

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