Shapley Values with H2O AutoML Example (ML Interpretability)
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Updated
Mar 17, 2019 - HTML
Shapley Values with H2O AutoML Example (ML Interpretability)
Sales Conversion Optimization MLOps: Boost revenue with AI-powered insights. Features H2O AutoML, ZenML pipelines, Neptune.ai tracking, data validation, drift analysis, CI/CD, Streamlit app, Docker, and GitHub Actions. Includes e-mail alerts, Discord/Slack integration, and SHAP interpretability. Streamline ML workflow and enhance sales performance.
Transformation of Akamai Logs with Spark ETL and discover of Values and similarities in logs used SparkML and H2O ML
Code & presentation for the 'H2O AutoML' short course at SDSS 2018 in Reston, VA
An investigation on the use of shapley explanations for unsupervised anomaly-detection models
Files for compiling my presentation about H2O.ai.
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