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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 Mar 2, 2022
  • Python
mljar-supervised

mljar / mljar-supervised

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Path normalization (train on Windows and deploy on Linux)

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ViacheslavDanilov
ViacheslavDanilov commented May 19, 2022

I trained models on Windows, then I tried to use them on Linux, however, I could not load them due to an incorrect path joining. During model loading, I got learner_path in the following format experiments_dir/model_1/100_LightGBM\\learner_fold_0.lightgbm. The last two slashes were incorrectly concatenated with the rest part of the path. In this regard, I would suggest adding something like `l

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awesome-gradient-boosting-papers

benedekrozemberczki / awesome-gradient-boosting-papers

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A curated list of gradient boosting research papers with implementations.

classifier machine-learning deep-learning random-forest h2o xgboost lightgbm gradient-boosting-machine adaboost decision-tree gradient-boosting-classifier classification-algorithm gradient-boosting boosting classification-trees xgboost-algorithm catboost gradient-boosted-trees classification-tree gradient-boosting-decision-trees
  • Updated Mar 2, 2022
  • Python

jphall663 / interpretable_machine_learning_with_python

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Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.

python data-science machine-learning data-mining h2o gradient-boosting-machine transparency decision-tree fairness lime accountability interpretability interpretable-ai interpretable-ml xai fatml interpretable interpretable-machine-learning iml machine-learning-interpretability
  • Updated Feb 10, 2021
  • Jupyter Notebook

microsoft / Forge

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milaan9 / Python_Decision_Tree_and_Random_Forest

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I've demonstrated the working of the decision tree-based ID3 algorithm. Use an appropriate data set for building the decision tree and apply this knowledge to classify a new sample. All the steps have been explained in detail with graphics for better understanding.

graphviz random-forest decision-tree decision-tree-classifier python4beginner decision-tree-algorithm id3-algorithm python-decisiontreeclassifier iris-classification cart-algorithm decision-tree-classification decision-tree-id3 python-tutorial-notebook python4everybody python-tutorial-github python4datascience python-decision-tree decision-tree-playgolf tutor-milaan9 dtreeviz
  • Updated May 15, 2022
  • Jupyter Notebook

kennedyCzar / STOCK-RETURN-PREDICTION-USING-KNN-SVM-GUASSIAN-PROCESS-ADABOOST-TREE-REGRESSION-AND-QDA

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Forecast stock prices using machine learning approach. A time series analysis. Employ the Use of Predictive Modeling in Machine Learning to Forecast Stock Return. Approach Used by Hedge Funds to Select Tradeable Stocks

pipeline random-forest prediction stock logistic-regression predictive-analysis stocks adaboost predictive-modeling algorithmic-trading decision-tree svm-classifier quadratic-discriminant-analysis parameter-tuning guassian-processes gridsearchcv knn-classifier
  • Updated Dec 3, 2020
  • Jupyter Notebook

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