#计算机科学#A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computa...
#计算机科学#A Lightweight Decision Tree Framework supporting regular algorithms: ID3, C4.5, CART, CHAID and Regression Trees; some advanced techniques: Gradient Boosting, Random Forest and Adaboost w/categorical ...
#计算机科学#Tensorflow implementation of Product-based Neural Networks. An extended version is at https://github.com/Atomu2014/product-nets-distributed.
This repository contains a notebook demonstrating a practical implementation of the so-called Entity Embedding for Encoding Categorical Features for Training a Neural Network.
#计算机科学#Scikit-Learn compatible transformer that turns categorical variables into dense entity embeddings.
#计算机科学#Encode Categorical Features (unmaintained)
#计算机科学#A Python framework for deploying recommendation models for form fields.
#计算机科学#A small tutorial to demonstrate the power of CatBoost Algorithm
Predicting the ideological direction of Supreme Court decisions: ensemble vs. unified case-based model
glmdisc Python package: discretization, factor level grouping, interaction discovery for logistic regression
#计算机科学#A mixed attributes predictive algorithm implemented in Python.
#计算机科学#This study creates machine learning models to predict the seriousness of car crashes using 2019 and 2020 crash reports from the publicly accessable database maintained by the Chicago Police Department...
#计算机科学#Multimodal deep learning package that uses both categorical and text-based features in a single deep architecture for regression and binary classification use cases.
Kaggle Categorical Feature Encoding Challenge II, private score 0.78795 (110 place)
#计算机科学#Generic encoding of record types