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awesome-decision-tree-papers

benedekrozemberczki / awesome-decision-tree-papers

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A collection of research papers on decision, classification and regression trees with implementations.

classifier machine-learning random-forest statistical-learning xgboost lightgbm gradient-boosting-machine ensemble-learning cart decision-tree tree-ensemble decision-tree-classifier gradient-boosting classification-trees decision-tree-learning decision-tree-model classification-model catboost regression-tree machine-learning-research
  • Updated Oct 29, 2020
  • Python
deep_classiflie_db

speediedan / deep_classiflie_db

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Deep_classiflie_db is the backend data system for managing Deep Classiflie metadata, analyzing Deep Classiflie intermediate datasets and orchestrating Deep Classiflie model training pipelines. Deep_classiflie_db includes data scraping modules for the initial model data sources. Deep Classiflie depends upon deep_classiflie_db for much of its analytical and dataset generation functionality but the data system is currently maintained as a separate repository here to maximize architectural flexibility. Depending on how Deep Classiflie evolves (e.g. as it supports distributed data stores etc.), it may make more sense to integrate deep_classiflie_db back into deep_classiflie. Currently, deep_classiflie_db releases are synchronized to deep_classiflie releases. To learn more, visit deepclassiflie.org.

nlp twitter-bot machine-learning database deep-learning ml pytorch data-engineering bokeh research-project albert fact-checking umap machine-learning-analysis interpretability disinformation transformer-architecture ml-framework machine-learning-research captum
  • Updated Oct 12, 2020
  • Jupyter Notebook
deep_classiflie

speediedan / deep_classiflie

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Deep Classiflie is a framework for developing ML models that bolster fact-checking efficiency. As a POC, the initial alpha release of Deep Classiflie generates/analyzes a model that continuously classifies a single individual's statements (Donald Trump) using a single ground truth labeling source (The Washington Post). For statements the model deems most likely to be labeled falsehoods, the @DeepClassiflie twitter bot tweets out a statement analysis and model interpretation "report"

nlp twitter-bot machine-learning database deep-learning ml pytorch data-engineering bokeh research-project albert fact-checking umap machine-learning-analysis interpretability disinformation transformer-architecture ml-framework machine-learning-research captum
  • Updated Oct 23, 2020
  • Python

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