Predictive Modeling
Showing 25 of 25 projects
A Julia machine learning framework providing a unified interface and meta-algorithms for over 200 models.
A Python library for probabilistic prediction using natural gradient boosting, built on scikit-learn.
A unified interface and infrastructure for machine learning in R, supporting classification, regression, clustering, and survival analysis.
A Python package for concise, transparent, and accurate predictive modeling with sklearn-compatible interpretable models.
A Python library for time series forecasting using scikit-learn compatible machine learning models.
A software implementation of factorization machines for estimating interactions between categorical variables in large datasets.
A Ruby library implementing the ID3 algorithm for decision tree learning with support for continuous and discrete datasets.
A model-agnostic toolkit for exploring and explaining the behavior of complex machine learning models in R and Python.
A deep learning toolkit for computational chemistry and drug design research with PyTorch backend.
A Ruby library for building and serving predictive models with support for PMML and integration with Python and R models.
Python implementation of the RuleFit algorithm for interpretable machine learning predictions using rule ensembles.
A BERT-based language model pretrained on clinical notes for predicting hospital readmissions and analyzing medical text.
A scikit-learn compatible Python library for probabilistic regression, survival analysis, and probability distributions.
An R package for automatic optimal predictor ensembling via cross-validation with dozens of machine learning algorithms.
A web interface and REST API for classification and regression using Support Vector Machine (SVM) and Support Vector Regression (SVR) algorithms.
A Python library for stacked generalization (ensemble learning) that supports scikit-learn, XGBoost, and Keras models with out-of-fold prediction saving.
A Ruby interface to XGBoost, providing high-performance gradient boosting for machine learning tasks.
Feature generation code for the Kaggle Acquire Valued Shoppers Challenge, focusing on customer behavior prediction.
Clojure bindings for the BigML.io API, enabling machine learning tasks and pipelines from Clojure code.
A machine learning project that predicts wine quality using R and MATLAB scripts with PCA visualization.
A machine learning solution for Amazon's access control challenge, predicting employee resource access needs.
A Julia package implementing online mini-batch learning algorithms for predictive modeling with GLMs and SVMs.
An implementation of Dell Zhang's solution to Wikipedia's Participation Challenge on Kaggle.
Code for the Best Buy competition at Kaggle, focusing on mobile contest big data analysis.
A Ruby gem for building generative machine learning models from time series data.
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