README.md

In yhhc2/machinelearnr: Package with various functions for preprocessing, clustering, and classification

machinelearnr

Overview

Author: Yong-Han Hank Cheng

This package provides functions for: 1. Preprocessing data. 2. Clustering with K-means or hierarchical clustering. 3. Classification with random forest.

The main functions for clustering are:

• HierarchicalClustering(): This can be used to create dendrograms. The linkage type and distance type used for clustering can be specified. Additionally, the stability of the clusters can also be calculated. The name of the observations are displayed at the leaves and additional coloring can be added to the leaves in addition to the cluster coloring.
• generate.3D.clustering.with.labeled.subgroup(): After kmeans clustering is performed, it is often helpful to visualize what the clusters look like. If 3 or more variables were used for clustering, PCA can be performed and the points can be plotted with the top 3 components to visualize what clusters look like in 3D.

The main functions for classification are:

• RandomForestAutomaticMtryAndNtree(): This uses the randomForest::randomForest function to create a random forest classifier. Additional code is included for optimizing mtry and ntree. Importantly, the default values for mtry and ntree are sensible values and actually is not required to be adjusted. For more rigorous explanations on hyperparameter tuning, use this paper and the R package provided by the authors: https://doi.org/10.1002/widm.1301, “Hyperparameters and tuning strategies for random forest” by Probst et al, 2019.

• RandomForestClassificationPercentileMatrixForPheatmap(): This creates random forest model on several smaller subset data sets of a larger data set and outputs results in the form of a pheatmap so that the random forest performance in terms of prediction accuracy and feature importance can be compared between each subset.

• CVPredictionsRandomForest(): This performs random forest classification using default hyperparameters and also uses cross-validation. The cross-validation fold can be specified by the user.The performance (predictions and feature importance determination) of the model are outputted.

• CVRandomForestClassificationMatrixForPheatmap(): This uses CVPredictionsRandomForest() on several smaller subset data sets of a larger data set and outputs results in the form of a pheatmap so that the random forest performance in terms of prediction accuracy and feature importance can be compared between each subset.

Installation

# Install the package from GitHub
devtools::install_github("yhhc2/machinelearnr")

# Load package
library("machinelearnr")

Usage

Source code: https://github.com/yhhc2/machinelearnr

Visit the package’s website: https://yhhc2.github.io/machinelearnr/

Function reference is located here: https://yhhc2.github.io/machinelearnr/reference/index.html

Visit this vignette for example output for each function usage: https://yhhc2.github.io/machinelearnr/articles/Examples.html

License

The machinelearnr package is licensed under the GPL (>=3) license. The logo is licensed under the CC BY 4.0 license.

yhhc2/machinelearnr documentation built on Dec. 23, 2021, 7:19 p.m.