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)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
#> CART: Classification and Regression Trees
#> ConditionalSuperLearner: Conditional SuperLearner
#> GAM: Generalized Additive Model
#> GLM: Generalized Linear Model
#> GLMNET: Elastic Net
#> HAL: Highly Adaptive Lasso
#> Isotonic: Isotonic Regression
#> MonotonicHAL: Monotonic Highly Adaptive Lasso
#> KNN: k-Nearest Neighbors
#> LightCART: Decision Tree
#> LightGBM: Gradient Boosting
#> LightRF: LightGBM Random Forest
#> LightRuleFit: LightGBM RuleFit
#> MARS: Multivariate Adaptive Regression Splines
#> MLP: Multilayer Perceptron
#> ModalityStacking: Per-Modality Stacked Ensemble
#> NNLS: Non-negative Least Squares
#> Ranger: Random Forest
#> SuperLearner: Cross-validated Stacked Ensemble
#> LinearSVM: Support Vector Machine with Linear Kernel
#> RadialSVM: Support Vector Machine with Radial Kernel
#> SPLS: Sparse Partial Least Squares
#> TabNet: Attentive Interpretable Tabular Learning
# Train with one of them, at its default hyperparameters:
# train(iris, hyperparameters = setup_LightGBM())
available_clustering()
#> CMeans: Fuzzy C-means Clustering
#> DBSCAN: Density-based spatial clustering of applications with noise
#> HardCL: Hard Competitive Learning
#> KMeans: K-Means Clustering
#> NeuralGas: Neural Gas Clustering
available_calibration()
#> Isotonic: Isotonic Regression
#> MonotonicHAL: Monotonic Highly Adaptive Lasso
# Calibrate with one of them:
# calibrate(mod, hyperparameters = setup_Isotonic())
available_decomposition()
#> ICA: Independent Component Analysis
#> Isomap: Isomap
#> NMF: Non-negative Matrix Factorization
#> PCA: Principal Component Analysis
#> tSNE: t-distributed Stochastic Neighbor Embedding
#> UMAP: Uniform Manifold Approximation and Projection