Using / reproducing DAC from the paper "Disentangled Attribution Curves for Interpreting Random Forests and Boosted Trees"
MIT License
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Random feature latent variable models in Python
Implementations of the machine learning algorithm with Python and numpy
Using / reproducing ACD from the paper "Hierarchical interpretations for neural network predictio...
Explore the double-descent phenomena in the context of system identification. Companion code to t...
机器学习原理
Text Classification Algorithms: A Survey
Interpretable text embeddings by asking LLMs yes/no questions
A python library for decision tree visualization and model interpretation.
Fast Random Kernelized Features: Support Vector Machine Classification for High-Dimensional IDC D...
Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-co...
A collection of research papers on decision, classification and regression trees with implementat...
Implementation of Machine Learning Algorithms
Adversarially Learned Inference in Pytorch
Package for causal inference in graphs and in the pairwise settings. Tools for graph structure re...
Extends scikit-learn with new models, transformers, metrics, plotting.