Philipp Probst's Projects
Automatic machine learning package with tuning tailored to the algorithms and runtime specification
Automatic tuning of SVM models
A minimal benchmark for scalability, speed and accuracy of commonly used open source implementations (R packages, Python scikit-learn, H2O, xgboost, Spark MLlib etc.) of the top machine learning algorithms for binary classification (random forests, gradient boosted trees, deep neural networks etc.).
Benchmark default learners of mlr on OpenML Datasets
With this script the monthly cran downloads of packages can be analysed
Getting an optimal set of defaults for common machine learning algorithms
exploratory data analysis using random forests
A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.
Some examples to work on the LRZ with R
mlr: Machine Learning in R
The mlr package online tutorial
Easy Hyper Parameter Optimization with mlr and mlrMBO.
Random Forest OOB Curves for any performance measure of mlr
Benchmarking datasets for regression based on OpenML
Paper for the description of the OpenML R-Bot
My Blog about tree-based methods:
:exclamation: This is a read-only mirror of the CRAN R package repository. quantregForest — Quantile Regression Forests
Quantile Regression for Ranger
A Fast Implementation of Random Forests
Benchmark hyperparameter study of random forest