Ensemble Automator

Conformal Prediction framework for Uncertainity Quantification of Stochastic Models.

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• Conformal prediction is a framework that quantifies uncertainity by estimating the confidence and credibility of test point predictions.
• Conformal prediction works using a nearest centroid classifier, along with computing non-conformal and p-value score.
• Currently, our model is able to accurately predict on new predictions with reliable accuracy on ANN, CNN, and DQN models.
• This is part of our project on ML interability via Actor-Critic networks.

To run our models:

• Fork/download this repo.
• cd src/code/pick_your_model
• run the respective jupyter notebook by going to terminal on the root directory of this project and entering jupyter notebook or in VSCode.

Requirements

0. TensorFlow
1. PyTorch
2. TorchVision
3. Numpy
4. gym
5. pandas
6. matplotlib
7. keras
8. time
9. tqdm