Print available algorithms for supervised learning, clustering, and decomposition.
Usage
available_supervised(verbosity = 1L)
available_clustering(verbosity = 1L)
available_calibration(verbosity = 1L)
available_decomposition(verbosity = 1L, traits = FALSE)Arguments
- verbosity
Integer: Verbosity level.
- traits
Logical: If TRUE,
available_decomposition()prints and returns the full trait table from decomposition_traits instead of the named list of descriptions.
Details
Each algorithm is set up with setup_{Algorithm}(), using the name printed
here: setup_LightGBM(), setup_KMeans(), setup_PCA(). Pass the result to
train, cluster, or decomp.
Examples
available_supervised()#> BART: Bayesian Additive Regression Trees (stochtree)
#> CART: CART decision tree (rpart)
#> ConditionalSuperLearner: Conditional SuperLearner: an oracle routes each case to one of a library of experts
#> GAM: GAM (generalized additive model)
#> GLM: GLM (generalized linear model)
#> GLMNET: Elastic net (glmnet)
#> GLMTree: Model-Based Recursive Partitioning: a tree with a GLM in each leaf
#> HAL: Highly Adaptive Lasso (hal9001)
#> Isotonic: Isotonic regression
#> MonotonicHAL: Monotonic Highly Adaptive Lasso (hal9001)
#> KNN: k-Nearest Neighbors (kknn)
#> LightCART: Single LightGBM tree (CART mode)
#> LightGBM: LightGBM gradient boosting
#> LightRF: LightGBM random forest
#> LightRuleFit: LightRuleFit (LightGBM rules + GLMNET)
#> LINAD: Linear Additive Tree
#> LINADForest: Bagged ensemble of Linear Additive Trees
#> MARS: Multivariate Adaptive Regression Splines (earth)
#> MLP: Multilayer perceptron (torch)
#> ModalityStacking: ModalityStacking: stacked ensemble with one learner per feature group
#> NNLS: Non-negative least squares (nnls)
#> Ranger: Ranger random forest
#> SuperLearner: SuperLearner: cross-validated stacked ensemble
#> LinearSVM: SVM with linear kernel (e1071)
#> RadialSVM: SVM with radial kernel (e1071)
#> SPLS: Sparse Partial Least Squares
#> TabNet: TabNet neural network# Train with one of them, at its default hyperparameters:
# train(iris, hyperparameters = setup_LightGBM())
available_clustering()#> CMeans: Fuzzy c-means clustering
#> DBSCAN: DBSCAN density-based clustering
#> GMM: Gaussian mixture model clustering
#> HOPACH: Hierarchical Ordered Partitioning and Collapsing Hybrid
#> HardCL: Hard competitive learning
#> KMeans: K-means clustering
#> NeuralGas: Neural Gas clustering
#> PAM: Partitioning Around Medoids
#> PAMK: Partitioning Around Medoids with estimation of the number of clusters
#> SpectralLaplace: Spectral clustering with an exponential kernel, which decays more slowly than the Gaussian and so keeps more weight on distant pairs
#> SpectralLocal: Spectral clustering with a Gaussian kernel whose width is set per case from the distance to its seventh nearest neighbor, which lets one clustering hold groups of differing density
#> SpectralRBF: Spectral clustering with a Gaussian kernel of one shared widthavailable_calibration()#> Isotonic: Isotonic regression
#> MonotonicHAL: Monotonic Highly Adaptive Lasso (hal9001)# Calibrate with one of them:
# calibrate(mod, hyperparameters = setup_Isotonic())
available_decomposition()#> Autoencoder: Autoencoder (torch)
#> ICA: Independent Component Analysis
#> Isomap: Isomap
#> MDS: Multidimensional Scaling
#> NMF: Non-negative Matrix Factorization
#> PCA: Principal Component Analysis
#> PCoA: Principal Coordinates Analysis
#> tSNE: t-Distributed Stochastic Neighbor Embedding
#> UMAP: Uniform Manifold Approximation and Projection
#> VariationalAutoencoder: Variational autoencoder (torch)available_decomposition(traits = TRUE)#> name description linear
#> 1 Autoencoder Autoencoder (torch) FALSE
#> 2 ICA Independent Component Analysis TRUE
#> 3 Isomap Isomap FALSE
#> 4 MDS Multidimensional Scaling FALSE
#> 5 NMF Non-negative Matrix Factorization TRUE
#> 6 PCA Principal Component Analysis TRUE
#> 7 PCoA Principal Coordinates Analysis FALSE
#> 8 tSNE t-Distributed Stochastic Neighbor Embedding FALSE
#> 9 UMAP Uniform Manifold Approximation and Projection FALSE
#> 10 VariationalAutoencoder Variational autoencoder (torch) FALSE
#> can_apply invertible orthogonal ordered deterministic preserves nonneg
#> 1 TRUE TRUE FALSE FALSE FALSE reconstruction FALSE
#> 2 TRUE TRUE FALSE FALSE FALSE variance FALSE
#> 3 FALSE FALSE FALSE FALSE TRUE global FALSE
#> 4 FALSE FALSE TRUE FALSE FALSE global FALSE
#> 5 TRUE TRUE FALSE FALSE FALSE reconstruction TRUE
#> 6 TRUE TRUE TRUE TRUE TRUE variance FALSE
#> 7 TRUE FALSE TRUE TRUE TRUE global FALSE
#> 8 FALSE FALSE FALSE FALSE FALSE local FALSE
#> 9 TRUE FALSE FALSE FALSE FALSE local FALSE
#> 10 TRUE TRUE FALSE FALSE FALSE reconstruction FALSE
#> threaded package
#> 1 TRUE torch
#> 2 FALSE fastICA
#> 3 FALSE vegan
#> 4 FALSE vegan
#> 5 FALSE NMF
#> 6 FALSE stats
#> 7 FALSE vegan
#> 8 TRUE Rtsne
#> 9 TRUE uwot
#> 10 TRUE torch