tree-based-methods
There are 25 repositories under tree-based-methods topic.
CN-TU/machine-learning-in-ebpf
This repository contains the code for the paper "A flow-based IDS using Machine Learning in eBPF", Contact: Maximilian Bachl
iuliivasilev/dev-survivors
Tree-based survival analysis from scratch
sliao7/CSE6740_Computational_Data_Analysis
All the course work of supervised and unsupervised algorithms and projects.
tugrulhkarabulut/Tree-Based-Methods
Implementation of Decision Tree and Ensemble Learning algorithms in Python with numpy
KarimABOUSSELHAM/ISLP-applied-solutions
Solutions of applied exercises contained in "An Introduction to Statistical Learning with Applications in Python", by Tibshirani et al, edition 2023
FilomKhash/Tree-based-paper
Codes for the paper On marginal feature attributions of tree-based models
gabrieldeolaguibel/ML-Projects
A collection of various applied Machine Learning and Artificial Intelligence projects I have done.
Lindahe0707/Customer-Loyalty-Analysis-From-Purchasing-Behavior
This is a customer loyalty analysis based on historical purchase behavior in R language.
aubin-tchoi/flappy_bird
Tree-based algorithms for solving a game of Flappy Bird.
eiliaJafari/tree-based-customer-churn-rate
Tree methods for customer churn prediction. Creating a model to predict whether or not a customer will Churn .
lakshyaag/ML-Tree-Based-Algorithms
Implementing Tree-based algorithms from scratch (Decision Tree, Random Forest, and Gradient Boosting) from scratch and comparing it to the scikit-learn implementation.
Marmingen/SML-lead-analysis
Analyzing the binary gender difference in lead roles using statistical machine learning
owenpb/Kaggle-Bike-Sharing-Prediction
Kaggle competition: predicting bikeshare demand with regression techniques. Linear/Lasso/Ridge Regression, KNN, Decision Tree, Random Forest, AdaBoost, XGBoost.
owenpb/Kaggle-Forest-Cover-Prediction
Kaggle competition: predicting forest cover type with multiclass classification algorithms. Logistic Regression, SVC, KNN, Decision Tree, Random Forest, XGBoost, AdaBoost, LightGBM, & Extra Trees.
SANTONLA/INTRODUCTION-TO-MACHINE-LEARNING-WITH-R
This is a repository with exercises extracted from the book "Introduction to machine learning with R" from Scott V. Burger. It will help you gain a solid foundation in machine learning principles. Using the R programming and then move into more advanced topics such as neural networks and tree-based methods.
Alex-Mak-MCW/Deposit_Subcriptions_Predictions_Project
Group academic research project focuses on predicting term deposit subscriptions for bank clients through data science, data analytics, and machine learning.
jasonmorkel/random_forests_exercise_ML
Random Forests Tree-Based Model in Machine Learning (exercise using Iris data)
paulinealvarado/stanford-stat-learning
Supervised learning and unsupervised in R, with a focus on regression and classification methods.
TommasoTarchi/proglie
Comparison of multiple machine learning algorithms for leaf classification.
yjyjpark/DS-with-R
Data Analysis with R
abhiram-ds/telecom_churn_case_study
Telecom Churn analysis using various tree based classification models
annalisaxamin/SL_homeworks
Homeworks for Statistical Learning course (Prof. Vinciotti) @ University of Trento
LucasO21/seoul-bikeshare-prediction
A machine learning project, predicting hourly bike rentals in Seoul.
tohid-yousefi/Prediction_Diabetes_Using_Classification_Machine_Learning_Algorithms
In this section we will be predicting diabetes using classification machine-learning algorithms
yuvalofek/FrequentistML
Linear & logistic regression, model assessment and selection, and gradient boosted trees