Flexible tool for bias detection, visualization, and mitigation
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Updated
Aug 29, 2022 - R
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explain-classifiers
Here are 5 public repositories matching this topic...
A scalable, explainable Java Naive Bayes Classifier that works either in memory or on persistent fast key-value store (MapDB, RocksDB or LevelDB)
rocksdb
leveldb
naive-bayes-classifier
naive-bayes-algorithm
mapdb
naive-bayes-classification
explainable-artificial-intelligence
explainable-ai
naivebayesclassifier
explain-classifiers
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Updated
Jun 14, 2023 - Java
Robust regression algorithm that can be used for explaining black box models (Python implementation)
python
machine-learning
research
sparse-regression
research-paper
explainable-ai
explainable-ml
interpretable-machine-learning
robust-regression
model-agnostic
black-box-explanations
local-explanations
explain-classifiers
slise
slise-algorithm
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Updated
May 11, 2024 - Python
Robust regression algorithm that can be used for explaining black box models (R implementation)
machine-learning
r
research
r-package
sparse-regression
research-paper
explainable-ai
explainable-ml
interpretable-machine-learning
robust-regression
model-agnostic
black-box-explanations
local-explanations
explain-classifiers
slise
slise-algorithm
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Updated
Aug 17, 2023 - R
Designed a Machine Learning model which takes newsgroup dataset and performs binary classification to predict if a given document has Atheistic or Christian sentiment. Used LIME library and PySpark. Performed feature selection to improve classifier’s performance.
feature-selection
pyspark
mllib
sparksql
python-3
binary-classification
lime
f1-score
newsgroups-dataset
explain-classifiers
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Updated
Apr 15, 2020 - Python
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