miceRanger: Fast Imputation with Random Forests in R
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Updated
Aug 24, 2022 - R
miceRanger: Fast Imputation with Random Forests in R
Scripts, tools and example data for mapping wetland ecosystems using data driven R statistical methods like Random Forests and open source GIS
This is the repository used to make the project titled 'Grass Pollen in Cape Town: A Comparison of Generalised Additive Models and Random Forests' by Sky Cope and Chloë Stipinovich.
Material associated to the publication project on local trees methods for classification
R code for "Predicting predator-prey interactions in terrestrial endotherms using random forest"
Data Analytics and Machine Learning in R. Linear-regression, Logistic-regression, Hierarchical-clustering, Boosting, Bagging, Random-forests, K-means-clustering, K-nearest-neighbors (K-N-N), Tree-pruning, Subset-selection, LDA, QDA, Support Vector Machines (SVM)
RFA package for implementing random forest adjustment.
Supervised learning and unsupervised in R, with a focus on regression and classification methods.
In-depth analysis about rminer package for regression. Project from my Applied Statistics and Data Analysis course in CS master degree.
Portfolio of machine learning projects
Predicts anticancer peptides using random forests trained on the n-gram encoded peptides. The implemented algorithm can be accessed from both the command line and shiny-based GUI.
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