There are 3 repositories under classification-trees topic.
A collection of research papers on decision, classification and regression trees with implementations.
A curated list of gradient boosting research papers with implementations.
Machine Learning notebooks for refreshing concepts.
PyTorch Implementation of "Distilling a Neural Network Into a Soft Decision Tree." Nicholas Frosst, Geoffrey Hinton., 2017.
python implementation of classification trees
A python implementation of the CART algorithm for decision trees
Vídeos e códigos do Universo Discreto ensinando o fundamental de Machine Learning em Python. Para mais detalhes, acompanhar a playlist listada.
Simple pixelwise skin detection using classification tree.
Moody's Bond Rating Classifier and USPHCI Economic Activity Forecast Modeling
A set of tools useful for doing experiments with fuzzy forests
Machine Learning (DD2421) at Royal Institute of Technology KTH
A collection of problems solved with machine learning algorithms, using R.
A random forest algorithm is implemented in Python from scratch to perform a classification analysis.
This project is focused on end to end application of Machine Learning methodologies to achieve best predicting accuracy. Our goal is to predict churn rate of customers for a telecom service provider based on service charges and usage data of customer.
Sistem Pendukung Keputusan untuk menentukan layak dan tidaknya seseorang mendapatkan bantuan PKH dengan menggunakan machine learning yaitu C4.5 dan K-Means
基于50万亚马逊美食评论数据集的评论分类系统 Review classification system based on 500 thousand Amazon gourmet review data
Classification on the Kobe Bryant Shot Selection dataset (https://www.kaggle.com/c/kobe-bryant-shot-selection/data) using Decision Trees
Performed segmentation analysis and predictive modeling on insurance broker performance to conclude a random forest model (highest AUC of 73%) predicted whether 2020 Gross Written Premium will increase or decrease from 2019 with a misclassification rate of 35%. Four classification models (classification trees, logistic regression, random forests, and support vector machines) were built, evaluated, and then tuned for prescriptive measures to analyze broker performance. Explored, visualized, and described five groups of brokers using principal component analysis.
A collection of mini projects in R that apply statistical and machine learning methods and tools to solve data-driven problems
Finding out whether it is possible to predict the quality of the wine using Random Forests, Neural Networks, Classification trees and other methods
My first attempt at Elixir, a basic implementation of decision trees.
Multivariate Environmental Statistics (BEE6300) R Code
Let’s practice and become familiar with classification algorithms.
Final project progress will be posted here.
BigData, Classification, Clustering, Graph analytics, Splunk, Spark, Neo4j, KNIME
Six machine learning models for predicting bladder cancer progression using Caret package
This Repo contains Decision Tree related various tasks
Using classification trees to predict a hand written digit.
ML projects, which I worked on utilising different machine learning algorithms.
Analyze the ATM dataset and predict the monthly withdrawal and rating of an ATM based on the features in the dataset 🏧
Focused customer retention programs
Identify Features Affecting Consumer Choice of Different Cuisines in the US