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extremely-randomized-trees

Here are 7 public repositories matching this topic...

kPPDERT_cloud

AminAminifar / kPPDERT_cloud

Extremely Randomized Trees with Privacy Preservation for Distributed Data (k-PPD-ERT)

machine-learning distributed-learning privacy-preserving-machine-learning secure-multiparty-computation extremely-randomized-trees

Updated Nov 13, 2024
Python

JNoel71 / NHL-Game-Prediction-Model

Predicting the 2021 NHL season with the use of data from the NHL API, and an extremely randomized trees model.

machine-learning sports prediction sports-analytics sports-predictions extremely-randomized-trees

Updated Jan 30, 2024
Python

cglamb / Voting_Ensemble_Spam

Applies Machine Learning approach to predict spam.

random-forest naive-bayes-classifier decision-trees support-vector-machines spam-filtering cnn-model gradient-boosting extreme-gradient-boosting bert-model adaptive-boosting-algorithm extremely-randomized-trees voting-ensemble ltsm-model

Updated Dec 2, 2024
Jupyter Notebook

techbrainwave / Unsupervised-Learning

Application of unsupervised learning and dimensionality reduction towards multiple problem sets.

machine-learning-algorithms expectation-maximization logistic-regression random-projections principal-component-analysis kmeans-clustering independent-component-analysis extremely-randomized-trees

Updated Dec 9, 2023
Python

louixnjeru / credit

Investigations into the credit default dataset

neural-network random-forest pytorch ensemble-learning decision-trees financial-analysis gradient-boosting adaptive-boosting-algorithm sckit-learn extremely-randomized-trees credit-default

Updated Jul 3, 2023
Jupyter Notebook

dmitana / ashrae_great_energy_predictor_III

FIIT Knowledge Discovery Project

machine-learning xgboost electricity-consumption-forecasting extremely-randomized-trees

Updated Sep 30, 2022
Jupyter Notebook

AlanP13 / Scalable-Analytics-for-Enterprise-Decisions-Vol7

Engineering-to-Research Monograph Series (Zenodo community). Companion code (Python) MIT, github.com/AlanP13. Grounded in author coursework (UC ITS-836, MSDS-532); written from scratch for publication.

big-data spark tabular-data mapreduce feature-engineering demand-forecasting gradient-boosting financial-operations enterprise-analytics extremely-randomized-trees tree-ensembles

Updated Jun 25, 2026
Python

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