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mljar-supervised

mljar / mljar-supervised

Star 2.9k

Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation

data-science machine-learning neural-network random-forest scikit-learn xgboost hyperparameter-optimization lightgbm ensemble feature-engineering decision-tree hyper-parameters automl automated-machine-learning catboost automatic-machine-learning mljar shap tuning-algorithm models-tuning
  • Updated Apr 15, 2024
  • Python
awesome-decision-tree-papers

benedekrozemberczki / awesome-decision-tree-papers

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Star 2.3k

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 16, 2024
  • Python
awesome-gradient-boosting-papers

benedekrozemberczki / awesome-gradient-boosting-papers

Sponsor
Star 981

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 16, 2024
  • Python

jphall663 / interpretable_machine_learning_with_python

Star 663

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 27, 2023
  • Jupyter Notebook

Western-OC2-Lab / Intrusion-Detection-System-Using-Machine-Learning

Star 326

Code for IDS-ML: intrusion detection system development using machine learning algorithms (Decision tree, random forest, extra trees, XGBoost, stacking, k-means, Bayesian optimization..)

machine-learning random-forest xgboost hyperparameter-optimization intrusion-detection lightgbm ensemble-learning kmeans autonomous-vehicles bayesian-optimization cyber-security decision-tree hpo network-security stacking intrusion-detection-system python-examples catboost cicids2017
  • Updated Oct 13, 2023
  • Jupyter Notebook

milaan9 / Python_Decision_Tree_and_Random_Forest

Star 242

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 Dec 9, 2022
  • Jupyter Notebook
100-Days-Of-ML-Code

harunurrashid97 / 100-Days-Of-ML-Code

Star 213

A day to day plan for this challenge. Covers both theoritical and practical aspects

python data-science machine-learning article linear-regression exploratory-data-analysis machine-learning-algorithms eda tutorials datascience data-preprocessing implementation decision-tree 100-days-of-code infographics regression-algorithms textsummarization siraj-raval-challenge vizualization 100daysofmlcode
  • Updated Feb 6, 2023
  • Jupyter Notebook

STMicroelectronics / STMems_Machine_Learning_Core

Star 154

Configuration files, examples and tools for the Machine Learning Core feature (MLC) available in STMicroelectronics MEMS sensors. Some examples of devices including MLC: LSM6DSOX, LSM6DSRX, ISM330DHCX, IIS2ICLX, LSM6DSO32X, ASM330LHHX, LSM6DSV16X, LIS2DUX12, LIS2DUXS12, LSM6DSV16BX, ASM330LHHXG1, LSM6DSV32X

machine-learning decision-tree mlc stmicroelectronics lsm6dsox mems-sensors lsm6dsrx ism330dhcx iis2iclx lsm6dso32x asm330lhhx lsm6dsv16x lis2dux12 lis2duxs12 lsm6dsv16bx asm330lhhxg1 lsm6dsv32x
  • Updated Mar 5, 2024
  • C

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