Pipelines for Machine Learning and Super Learning


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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

-- 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