augmented-interpretable-models
handson-ml
| augmented-interpretable-models | handson-ml | |
|---|---|---|
| 1 | 1 | |
| 37 | 25,094 | |
| - | - | |
| 7.4 | 0.0 | |
| 19 days ago | 7 months ago | |
| Jupyter Notebook | Jupyter Notebook | |
| MIT License | Apache License 2.0 |
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augmented-interpretable-models
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[R] Emb-GAM: an Interpretable and Efficient Predictor using Pre-trained Language Models
Deep learning models have achieved impressive prediction performance but often sacrifice interpretability, a critical consideration in high-stakes domains such as healthcare or policymaking. In contrast, generalized additive models (GAMs) can maintain interpretability but often suffer from poor prediction performance due to their inability to effectively capture feature interactions. In this work, we aim to bridge this gap by using pre-trained neural language models to extract embeddings for each input before learning a linear model in the embedding space. The final model (which we call Emb-GAM) is a transparent, linear function of its input features and feature interactions. Leveraging the language model allows Emb-GAM to learn far fewer linear coefficients, model larger interactions, and generalize well to novel inputs (e.g. unseen ngrams in text). Across a variety of NLP datasets, Emb-GAM achieves strong prediction performance without sacrificing interpretability. All code is made available on Github.
handson-ml
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need a book recommendation for machine learning on python
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow is often recommended. You can check out the GitHub repo first: https://github.com/ageron/handson-ml
What are some alternatives?
language-planner - Official Code for "Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents"
Spotify_Song_Recommender - This project leverages spotify's api and provided user playlists to create and tune a neural network model that generates song recommendations based off of song data in provided playlists.
shap - A game theoretic approach to explain the output of any machine learning model. [Moved to: https://github.com/shap/shap]
AeroPython - Classical Aerodynamics of potential flow using Python and Jupyter Notebooks
scikit-learn-ts - Powerful machine learning library for Node.js – uses Python's scikit-learn under the hood.
Machine-Learning-Specialization-Coursera - Contains Solutions and Notes for the Machine Learning Specialization By Stanford University and Deeplearning.ai - Coursera (2022) by Prof. Andrew NG
DeepLearning - Contains all my works, references for deep learning
ML-Workspace - 🛠 All-in-one web-based IDE specialized for machine learning and data science.
gan-vae-pretrained-pytorch - Pretrained GANs + VAEs + classifiers for MNIST/CIFAR in pytorch.
Twitter-sentiment-analysis - A sentiment analysis model trained with Kaggle GPU on 1.6M examples, used to make inferences on 220k tweets about Messi and draw insights from their results.
AutoCog - Automaton & Cognition
python-machine-learning-book-3rd-edition - The "Python Machine Learning (3rd edition)" book code repository