Fast boosting with AdaBoost and Bandit
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Updated
Sep 19, 2019 - Jupyter Notebook
Fast boosting with AdaBoost and Bandit
Supervised learning and unsupervised in R, with a focus on regression and classification methods.
TP2 de Aprendizado de Máquina (2021/1) no DCC/UFMG.
This repository consists of projects on Big Data and Data Analytics as a part of our curriculum for PGDM- BDA (4th Term) at FORE School of Management, New Delhi.
Industrial-level analysis for predicting user retention
Predicting Colorado forest cover types using diverse ML models for classification. Baseline creation, feature selection, comparison, and tuning optimize accuracy in this University of Ottawa Master's Machine Learning course final project (2023).
Implementation of popular ensembles methods in C++
Analytics Vidhya AmExpert ML Competition
This repository contains various assignments that I have done as a part of the Machine Learning course.
Boosting is one of the methods contained in Ensemble Learning. This technique makes it possible to combine several models into one more robust model.
Detecting the alertness of a driver using an ML model of KNN and Logistic Regression
Open source gradient boosting library
This repository is associated with interpretable/explainable ML model for liquefaction potential assessment of soils. This model is developed using XGBoost and SHAP.
Analyze the data and come up with a predictive model to determine if a customer will leave the credit card services or not and the reason behind it
This notebook explores fraud detection using various machine learning techniques.
Machine Learning exercises for my subject of Machine Learning at University of Granada (UGR).
Implementing random forest models in R with bagging and boosting.
Applying predictive models on "Framingham Heart Study" dataset.
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