Lists (7)
Stars
A game theoretic approach to explain the output of any machine learning model.
Automatic extraction of relevant features from time series:
H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K…
Evidently is an open-source ML and LLM observability framework. Evaluate, test, and monitor any AI-powered system or data pipeline. From tabular data to Gen AI. 100+ metrics.
Create delightful software with Jupyter Notebooks
A collection of infrastructure and tools for research in neural network interpretability.
A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.
ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its go…
A python library for decision tree visualization and model interpretation.
A high performance implementation of HDBSCAN clustering.
A library for debugging/inspecting machine learning classifiers and explaining their predictions
An open-source data logging library for machine learning models and data pipelines. 📚 Provides visibility into data quality & model performance over time. 🛡️ Supports privacy-preserving data collec…
Algorithms for outlier, adversarial and drift detection
Hamilton helps data scientists and engineers define testable, modular, self-documenting dataflows, that encode lineage/tracing and metadata. Runs and scales everywhere python does.
2-2000x faster ML algos, 50% less memory usage, works on all hardware - new and old.
Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).
Tigramite is a python package for causal inference with a focus on time series data. The Tigramite documentation is at
A scikit-learn-compatible module to estimate prediction intervals and control risks based on conformal predictions.
A collection of reference Jupyter notebooks and demo AI/ML applications for enterprise use cases: marketing, pricing, supply chain, smart manufacturing, and more.
Framework-agnostic implementation for state-of-the-art saliency methods (XRAI, BlurIG, SmoothGrad, and more).
Automatically build ARIMA, SARIMAX, VAR, FB Prophet and XGBoost Models on Time Series data sets with a Single Line of Code. Created by Ram Seshadri. Collaborators welcome.
machine learning with logical rules in Python
CUDA-accelerated GIS and spatiotemporal algorithms
Attributing predictions made by the Inception network using the Integrated Gradients method