Starcraft 2 replay parsing, storage, and analysis
-
Updated
Feb 1, 2015 - TeX
Starcraft 2 replay parsing, storage, and analysis
Applying machine learning techniques with R to Census Income data set, a.k.a Adult data set.
Using machine learning to analyse the ravelry yarn dataset
A project to compare the performance of Random Forests against Support Vector Machine using a wine dataset from Kaggle. Metrics such as precision, recall, f1-score, loss and the 95% confidence interval of the true risk were compared.
Python implementation of the perceptron algorithm
Contact: Maximilian Bachl, Alexander Hartl. Explores defenses against backdoors and poisoning attacks for Intrusion Detection Systems. Code for "EagerNet" is in the "eager" branch.
Feature Kernel description and implementation details
Performing different models on a dataset regarding National Park biodiversity and the number of visitors.
Repo for Machine Learning CS 5350
A seminary paper intended to give a brief introduction on the topic of "Boosting, Bagging and Ensemble learning".
Machine Learning case study including an exploratory data analysis and fitting a Decision Tree, Random Forest, and XGBoost Model. Interactive notebook with outputs and visualizations: https://yaldan.github.io/ml_case_study/
Project about the use of Supervised (Machine) Learning techniques to predict the popularity of a tweet about food.
Online tutorial on how to use Ensemble Machine Learning for spatial and spatiotemporal interpolation / predictions
Master thesis in Incomplete time-series classification methods
LaTeX source file for my Computer Science Thesis "Clinical Data Management Processes and Predictive Machine Learning Models Development for Diagnosis and Rehabilitation in the Cardiovascular Domain", which spans over 100 pages. Research was conducted in collaboration with the multinational company Dedalus
Our study focused on using the Big Five personality inventory to predict traits from students' smartphone sensor data collected over 2 months under the Horizon Europe project. Through correlation analyses and machine learning with cross-validation, we showed that predictions are reliable and accurate enough for practical use.
benchmarking the aorsf package
MIT PRIMES Research Project 2024
Add a description, image, and links to the random-forest topic page so that developers can more easily learn about it.
To associate your repository with the random-forest topic, visit your repo's landing page and select "manage topics."