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...
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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May 11, 2024 - Python
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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Jun 14, 2023 - Java
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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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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Apr 15, 2020 - Python
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