# Pipelines for Machine Learning and Super Learning

## Documentation for package ‘sl3’ version 1.4.4

- DESCRIPTION file.
- Package NEWS.

## Help Pages

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

## -- T --

train_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.

## -- 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