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ensemble-learning

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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 opensource r big-data spark deep-learning hadoop random-forest gpu naive-bayes h2o distributed pca gbm ensemble-learning automl h2o-automl
  • Updated May 30, 2024
  • Jupyter Notebook

jmarihawkins / classification-challenge

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The purpose of this project is to develop and compare two machine learning models to detect spam emails. Spam detection is a crucial task in email filtering systems to protect users from unwanted and potentially harmful emails. The project involves using a dataset containing various features extracted from email content.

python data-science machine-learning natural-language-processing sklearn text-analysis pandas ensemble-learning logistic-regression data-preprocessing spam-detection binary-classification feature-scaling model-evaluation random-forest-classifier accuracy-score standardscaler train-test-split uci-machine-learning-repository
  • Updated May 29, 2024
  • Jupyter Notebook

SarveshChandra / Machine-Learning-and-Artificial-Intelligence-Case-Studies

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AIML Projects

statistics computer-vision deep-learning random-forest feature-extraction batch-normalization neural-networks supervised-learning pca recommendation-system ensemble-learning object-detection adaboost decision-trees feature-engineering unsupervised-learning support-vector-machines object-recognition knearest-neighbor-algorithm gradient-boosting
  • Updated May 26, 2024
  • Jupyter Notebook

shubhomoydas / ad_examples

Star 835

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 Convol…

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 May 22, 2024
  • Python

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