ODT-with-noisy-outcomes
Decision Tree Algorithm
An implementation of an optimal decision tree algorithm with noisy outcomes, specifically tailored to simulate real-world decision-making under uncertainty
NeuIPS2019: Optimal Decision Tree with noisy outcomes
0 stars
1 watching
1 forks
Language: Python
last commit: over 5 years ago
Linked from 1 awesome list
Related projects:
Repository | Description | Stars |
---|---|---|
| A Python implementation of an algorithm for constructing decision trees with regularization and various bounding functions to accelerate the search process. | 101 |
| This C++ project provides an implementation of decision tree algorithms for classification tasks | 7 |
| An algorithm for inferring optimal binary decision trees in C++ and wrapped by a Python interface | 62 |
| An implementation of incremental decision tree algorithms and ensemble methods for efficient machine learning on streaming data | 100 |
| An R implementation of Hellinger distance decision tree algorithm. | 10 |
| A Julia package implementing popular machine learning algorithms | 8 |
| An implementation of a binary decision tree using ActionScript 3.0 for AI bot/agent in video games. | 3 |
| A Python library providing a lightweight framework for building decision trees with categorical feature support | 463 |
| An implementation of Gradient Boosted Decision Trees with sparse output for high-dimensional data | 0 |
| A Go-based decision tree library allowing custom rule-based decision making | 37 |
| An implementation of robust decision tree based models against adversarial examples using the XGBoost framework. | 67 |
| An implementation of the ID3 algorithm for building decision trees | 1,438 |
| A Java implementation of a fairness-aware decision tree classifier for online stream-based classification | 8 |
| An implementation of a method to improve classification accuracy on noisy labels by reweighting their importance | 39 |
| An open-source system that enables secure and private machine learning predictions using decision tree models. | 25 |