flytxtds / AutoGBT
AutoGBT is used for AutoML in a lifelong machine learning setting to classify large volume high cardinality data streams under concept-drift. AutoGBT was developed by a joint team ('autodidact.ai') from Flytxt, Indian Institute of Technology Delhi and CSIR-CEERI as a part of NIPS 2018 AutoML for Lifelong Machine Learning Challenge.
☆114Updated 5 years ago
Alternatives and similar repositories for AutoGBT
Users that are interested in AutoGBT are comparing it to the libraries listed below
Sorting:
- Factorization Machine for regression and classification☆98Updated 7 years ago
- ☆70Updated 5 years ago
- ☆74Updated 6 years ago
- Gradient Boosting With Piece-Wise Linear Trees☆152Updated last year
- Extension of the awesome XGBoost to linear models at the leaves☆78Updated 5 years ago
- Preparing continuous features for neural networks with GaussRank☆45Updated 7 years ago
- xgboost Extension for Easy Ranking & TreeFeature☆125Updated 5 years ago
- Discretization with Fayyad and Irani's minimum description length principle criterion (MDLPC)☆60Updated 6 years ago
- A simple, extensible library for developing AutoML systems☆175Updated last year
- Library for machine learning stacking generalization.☆117Updated 6 years ago
- Development Repository for GPU-accelerated GBDT training☆60Updated 8 years ago
- An AutoML pipeline selection system to quickly select a promising pipeline for a new dataset.☆82Updated 3 years ago
- Tensorflow implementation of a Tree☆36Updated 5 years ago
- Public solution for AutoSeries competition☆72Updated 5 years ago
- Solution to Corporación Favorita Grocery Sales Forecasting Competition☆28Updated 7 years ago
- 2nd Place Solution for Kaggle Porto Seguro's Safe Driver Prediction☆155Updated 5 years ago
- This is the official clone for the implementation of the NIPS18 paper Multi-Layered Gradient Boosting Decision Trees (mGBDT) .☆103Updated 6 years ago
- An example of using a discriminator to correct for a difference in the distributions between the training and test data.☆67Updated 8 years ago
- 4th Place Solution for Kaggle Competition: Quora Insincere Questions Classification☆50Updated 6 years ago
- Supporting code for the paper "Finding Influential Training Samples for Gradient Boosted Decision Trees"☆67Updated 11 months ago
- This notebook shows how to implement LibFM in Keras and how it was used in the Talking Data competition on Kaggle.☆189Updated 7 years ago
- ☆250Updated 3 years ago
- ☆95Updated last year
- An experimental Python package that reimplements AutoGBT using LightGBM and Optuna.☆82Updated 6 years ago
- Code for WWW'19 "Unbiased LambdaMART: An Unbiased Pairwise Learning-to-Rank Algorithm", which is based on LightGBM☆225Updated 5 years ago
- The final code submission of Meta_Learners, which won the second place in NIPS 2018 AutoML Challenge☆17Updated 6 years ago
- Solution to the Outbrain Click Prediction competition☆148Updated 8 years ago
- TalkingData AdTracking Fraud Detection Challenge☆104Updated 7 years ago
- Home repository for the Regularized Greedy Forest (RGF) library. It includes original implementation from the paper and multithreaded one…☆381Updated 3 years ago
- Avito Demand Prediction Challenge☆56Updated 6 years ago