Pipelines for Machine Learning and Super Learning

Documentation for package ‘sl3’ version 1.4.4

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A B C D F I L M P R S T U V W

-- A --

args_to_list

Get all arguments of parent call (both specified and defaults) as list

-- B --

bsds	Bicycle sharing time series dataset

-- C --

cpp	Subset of growth data from the collaborative perinatal project (CPP)
cpp_1yr	Subset of growth data from the collaborative perinatal project (CPP)
cpp_imputed	Subset of growth data from the collaborative perinatal project (CPP)
customize_chain	Customize chaining for a learner
Custom_chain	Customize chaining for a learner
custom_ROCR_risk	FACTORY RISK FUNCTION FOR ROCR PERFORMANCE MEASURES WITH BINARY OUTCOMES
CV_lrnr_sl	Estimates cross-validated risk of the Super Learner
cv_risk	Cross-validated Risk Estimation

-- D --

debugonce_predict	Helper functions to debug sl3 Learners
debugonce_train	Helper functions to debug sl3 Learners
debug_predict	Helper functions to debug sl3 Learners
debug_train	Helper functions to debug sl3 Learners
default_metalearner	Automatically Defined Metalearner
define_h2o_X	h2o Model Definition
delayed_learner_fit_chain	Learner helpers
delayed_learner_fit_predict	Learner helpers
delayed_learner_process_formula	Learner helpers
delayed_learner_subset_covariates	Learner helpers
delayed_learner_train	Learner helpers
delayed_make_learner	Learner helpers
density_dat	Simulated data with continuous exposure
dt_expand_factors	Convert Factors to indicators

-- F --

factor_to_indicators

Convert Factors to indicators

-- I --

importance	Importance Extract variable importance measures produced by 'randomForest' and order in decreasing order of importance.
importance_plot	Variable Importance Plot
inverse_sample	Inverse CDF Sampling

-- L --

learner_fit_chain	Learner helpers
learner_fit_predict	Learner helpers
learner_process_formula	Learner helpers
learner_subset_covariates	Learner helpers
learner_train	Learner helpers
loss_functions	Loss Function Definitions
loss_loglik_binomial	Loss Function Definitions
loss_loglik_multinomial	Loss Function Definitions
loss_loglik_true_cat	Loss Function Definitions
loss_squared_error	Loss Function Definitions
loss_squared_error_multivariate	Loss Function Definitions
Lrnr_arima	Univariate ARIMA Models
Lrnr_bartMachine	bartMachine: Bayesian Additive Regression Trees (BART)
Lrnr_base	Base Class for all sl3 Learners
Lrnr_bayesglm	Bayesian Generalized Linear Models
Lrnr_bilstm	Bidirectional Long short-term memory Recurrent Neural Network (LSTM)
Lrnr_bound	Bound Predictions
Lrnr_caret	Wrapping Learner for Package Caret
Lrnr_cv	Fit/Predict a learner with Cross Validation
Lrnr_cv_selector	Cross-Validated Selector
Lrnr_dbarts	Discrete Bayesian Additive Regression Tree sampler
Lrnr_define_interactions	Define interactions terms
Lrnr_density_discretize	Density from Classification
Lrnr_density_hse	Density Estimation With Mean Model and Homoscedastic Errors
Lrnr_density_semiparametric	Density Estimation With Mean Model and Homoscedastic Errors
Lrnr_earth	Earth: Multivariate Adaptive Regression Splines
Lrnr_expSmooth	Exponential Smoothing state space model
Lrnr_ga	Nonlinear Optimization via Genetic Algorithm (GA)
Lrnr_gam	GAM: Generalized Additive Models
Lrnr_gbm	GBM: Generalized Boosted Regression Models
Lrnr_glm	Generalized Linear Models
Lrnr_glmnet	GLMs with Elastic Net Regularization
Lrnr_glm_fast	Computationally Efficient Generalized Linear Model (GLM) Fitting
Lrnr_grf	Generalized Random Forests Learner
Lrnr_gru_keras	Recurrent Neural Network with Gated Recurrent Unit (GRU) with Keras
Lrnr_gts	Grouped Time-Series Forecasting
Lrnr_h2o_classifier	Grid Search Models with h2o
Lrnr_h2o_glm	h2o Model Definition
Lrnr_h2o_grid	Grid Search Models with h2o
Lrnr_h2o_mutator	Grid Search Models with h2o
Lrnr_hal9001	Scalable Highly Adaptive Lasso (HAL)
Lrnr_haldensify	Conditional Density Estimation with the Highly Adaptive LASSO
Lrnr_HarmonicReg	Harmonic Regression
Lrnr_hts	Hierarchical Time-Series Forecasting
Lrnr_independent_binomial	Classification from Binomial Regression
Lrnr_lightgbm	LightGBM: Light Gradient Boosting Machine
Lrnr_lstm_keras	Long short-term memory Recurrent Neural Network (LSTM) with Keras
Lrnr_mean	Fitting Intercept Models
Lrnr_multiple_ts	Stratify univariable time-series learners by time-series
Lrnr_multivariate	Multivariate Learner
Lrnr_nnet	Feed-Forward Neural Networks and Multinomial Log-Linear Models
Lrnr_nnls	Non-negative Linear Least Squares
Lrnr_optim	Optimize Metalearner according to Loss Function using optim
Lrnr_pca	Principal Component Analysis and Regression
Lrnr_pkg_SuperLearner	Use SuperLearner Wrappers, Screeners, and Methods, in sl3
Lrnr_pkg_SuperLearner_method	Use SuperLearner Wrappers, Screeners, and Methods, in sl3
Lrnr_pkg_SuperLearner_screener	Use SuperLearner Wrappers, Screeners, and Methods, in sl3
Lrnr_polspline	Polyspline - multivariate adaptive polynomial spline regression (polymars) and polychotomous regression and multiple classification (polyclass)
Lrnr_pooled_hazards	Classification from Pooled Hazards
Lrnr_randomForest	Random Forests
Lrnr_ranger	Ranger: Fast(er) Random Forests
Lrnr_revere_task	Learner that chains into a revere task
Lrnr_rpart	Learner for Recursive Partitioning and Regression Trees.
Lrnr_rugarch	Univariate GARCH Models
Lrnr_screener_augment	Augmented Covariate Screener
Lrnr_screener_coefs	Coefficient Magnitude Screener
Lrnr_screener_correlation	Correlation Screening Procedures
Lrnr_screener_importance	Variable Importance Screener
Lrnr_sl	The Super Learner Algorithm
Lrnr_solnp	Nonlinear Optimization via Augmented Lagrange
Lrnr_solnp_density	Nonlinear Optimization via Augmented Lagrange
Lrnr_stratified	Stratify learner fits by a single variable
Lrnr_subset_covariates	Learner with Covariate Subsetting
Lrnr_svm	Support Vector Machines
Lrnr_tsDyn	Nonlinear Time Series Analysis
Lrnr_ts_weights	Time-specific weighting of prediction losses
Lrnr_xgboost	xgboost: eXtreme Gradient Boosting

-- M --

make_learner	Base Class for all sl3 Learners
make_learner_stack	Make a stack of sl3 learners
make_sl3_Task	Define a Machine Learning Task
metalearners	Combine predictions from multiple learners
metalearner_linear	Combine predictions from multiple learners
metalearner_linear_multinomial	Combine predictions from multiple learners
metalearner_linear_multivariate	Combine predictions from multiple learners
metalearner_logistic_binomial	Combine predictions from multiple learners

-- P --

pack_predictions	Pack multidimensional predictions into a vector (and unpack again)
Pipeline	Pipeline (chain) of learners.
pooled_hazard_task	Generate A Pooled Hazards Task from a Failure Time (or Categorical) Task
prediction_plot	Plot predicted and true values for diganostic purposes
predict_classes	Predict Class from Predicted Probabilities

-- R --

risk	Risk Estimation
risk_functions	FACTORY RISK FUNCTION FOR ROCR PERFORMANCE MEASURES WITH BINARY OUTCOMES

-- S --

safe_dim	dim that works for vectors too
Shared_Data	Container Class for data.table Shared Between Tasks
sl3Options	Querying/setting a single 'sl3' option
sl3_debug_mode	Helper functions to debug sl3 Learners
sl3_list_learners	List sl3 Learners
sl3_list_properties	List sl3 Learners
sl3_revere_Task	Revere (SplitSpecific) Task
sl3_Task	Define a Machine Learning Task
Stack	Learner Stacking
subset_folds	Make folds work on subset of data

Subset Tasks for CV THe functions use origami folds to subset tasks. These functions are used by Lrnr_cv (and therefore other learners that use Lrnr_cv). So that nested cv works properly, currently the subsetted task objects do not have fold structures of their own, and so generate them from defaults if nested cv is requested.

-- U --

undebug_learner	Helper functions to debug sl3 Learners
undocumented_learner	Undocumented Learner
unpack_predictions	Pack multidimensional predictions into a vector (and unpack again)

-- V --

validation_task	Subset Tasks for CV THe functions use origami folds to subset tasks. These functions are used by Lrnr_cv (and therefore other learners that use Lrnr_cv). So that nested cv works properly, currently the subsetted task objects do not have fold structures of their own, and so generate them from defaults if nested cv is requested.
Variable_Type	Specify Variable Type
variable_type	Specify Variable Type

-- W --

write_learner_template

Generate a file containing a template 'sl3' Learner