Easy Custom Losses for Tree Boosters using Pytorch
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Updated
Feb 28, 2021 - Python
Easy Custom Losses for Tree Boosters using Pytorch
No-Caffeine-No-Gain's Deep Knowledge Tracing (DKT)
LGBM and logistic regression for prediction of customers' second time transaction for an online market app.
Crypto & Stock* price prediction with regression models.
A project that demonstrates the use of the lgbm C++ API to perform inference without any python dependencies.
A collection of LightGBM callbacks. (DART early stopping, tqdm progress bar)
Find LGBM Hyperparams and train the model
Microsoft Malware Prediction Challenge. 8th position solution.
Predict the quality of wikipedia articles via Machine Learning
Music Genre Recommender website that can identify and recommend 10 different genres of music using Light Gradient Boosting Machine (LGBM). An accuracy of 90% was achieved on the test set by tuning the hyperparameters of the model with Optuna.
Repository for the "Google Analytics Customer Revenue Prediction" Kaggle competition.
Kaggle Competition - Analysis and prediction of PUBG players' finishing placement based on their final stats
This repository contains the code to build a prediction engine for London housing prices
Project for applied classical ML course at the Weizmann institute
In this project I used basic classification algorithms including random forest, xgboost and decision tree to reach the best solution, finding out how many survived in the titanic accident. I use Kaggles free GPUs and Datasets in this project. I used different feature engineering techniques to clean my data
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