Defining A Task

make_sl3_Task()

Define a Machine Learning Task

variable_type()

Specify variable type

Finding Learners

sl3_list_properties() sl3_list_learners()

List sl3 Learners

sl3 Learners

Lrnr_HarmonicReg

Harmonic Regression

Lrnr_arima

Univariate ARIMA Models

Lrnr_bartMachine

BART Machine Learner

make_learner()

Base Class for all sl3 Learners.

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

Lrnr_gam

Generalized Additive Models

Lrnr_gbm

GBM - generalized boosted regression models

Lrnr_glm

Generalized Linear Models

Lrnr_glm_fast

Computationally Efficient GLMs

Lrnr_glmnet

GLMs with Elastic Net Regularization

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

define_h2o_X()

h2o Model Definition

Lrnr_h2o_grid

Grid Search Models with h2o

Lrnr_hal9001

The Scalable Highly Adaptive Lasso

Lrnr_haldensify

Conditional Density Estimation with the Highly Adaptive LASSO

Lrnr_hts

Hierarchical Time-Series Forecasting

Lrnr_independent_binomial

Classification from Binomial Regression

Lrnr_lstm

Long short-term memory Recurrent Neural Network (LSTM)

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_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 - A Fast Implementation of 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

Lrnr_pkg_SuperLearner

Use SuperLearner Wrappers, Screeners, and Methods, in sl3

Composing Learners

Pipeline

Pipeline (chain) of learners.

Stack

Learner Stacking

customize_chain()

Customize chaining for a learner

Loss functions

loss_squared_error() loss_loglik_true_cat() loss_loglik_binomial() loss_loglik_multinomial() loss_squared_error_multivariate()

Loss Function Definitions

risk()

Risk Estimation

Metalearner functions

metalearner_logistic_binomial() metalearner_linear() metalearner_linear_multivariate() metalearner_linear_multinomial()

Combine predictions from multiple learners

Helpful for Defining Learners

write_learner_template()

Generate a file containing a template sl3 Learner

args_to_list()

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

call_with_args()

Call with filtered argument list

true_obj_size()

Estimate object size using serialization

safe_dim()

dim that works for vectors too

delayed_make_learner() learner_train() delayed_learner_train() learner_fit_predict() delayed_learner_fit_predict() learner_fit_chain() delayed_learner_fit_chain() learner_subset_covariates() delayed_learner_subset_covariates()

Learner helpers

Sample Datasets

cpp cpp_imputed

Subset of growth data from the collaborative perinatal project (CPP)

cpp_1yr

Subset of growth data from the collaborative perinatal project (CPP)

bsds

Bicycle sharing time series dataset

density_dat

Simulated data with continuous exposure

Miscellaneous

sl3Options()

Querying/setting a single sl3 option

Index

CV_lrnr_sl()

Estimate Cross-Validated Risk of Super Learner

customize_chain()

Customize chaining for a learner

Lrnr_HarmonicReg

Harmonic Regression

Lrnr_arima

Univariate ARIMA Models

Lrnr_bartMachine

BART Machine Learner

make_learner()

Base Class for all sl3 Learners.

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

Lrnr_gam

Generalized Additive Models

Lrnr_gbm

GBM - generalized boosted regression models

Lrnr_glm

Generalized Linear Models

Lrnr_glm_fast

Computationally Efficient GLMs

Lrnr_glmnet

GLMs with Elastic Net Regularization

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

define_h2o_X()

h2o Model Definition

Lrnr_h2o_grid

Grid Search Models with h2o

Lrnr_hal9001

The Scalable Highly Adaptive Lasso

Lrnr_haldensify

Conditional Density Estimation with the Highly Adaptive LASSO

Lrnr_hts

Hierarchical Time-Series Forecasting

Lrnr_independent_binomial

Classification from Binomial Regression

Lrnr_lstm

Long short-term memory Recurrent Neural Network (LSTM)

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_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 - A Fast Implementation of 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

Pipeline

Pipeline (chain) of learners.

Shared_Data

Container Class for data.table Shared Between Tasks

Stack

Learner Stacking

Lrnr_pkg_SuperLearner

Use SuperLearner Wrappers, Screeners, and Methods, in sl3

args_to_list()

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

bsds

Bicycle sharing time series dataset

cpp cpp_imputed

Subset of growth data from the collaborative perinatal project (CPP)

cpp_1yr

Subset of growth data from the collaborative perinatal project (CPP)

train_task() 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.

cv_risk()

Cross-validated Risk Estimation

debug_train() debugonce_train() debug_predict() debugonce_predict() sl3_debug_mode() undebug_learner()

Helper functions to debug sl3 Learners

default_metalearner()

Automatically Defined Metalearner

density_dat

Simulated data with continuous exposure

factor_to_indicators() dt_expand_factors()

Convert Factors to indicators

importance() 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

delayed_make_learner() learner_train() delayed_learner_train() learner_fit_predict() delayed_learner_fit_predict() learner_fit_chain() delayed_learner_fit_chain() learner_subset_covariates() delayed_learner_subset_covariates()

Learner helpers

sl3_list_properties() sl3_list_learners()

List sl3 Learners

loss_squared_error() loss_loglik_true_cat() loss_loglik_binomial() loss_loglik_multinomial() loss_squared_error_multivariate()

Loss Function Definitions

make_learner_stack()

Make a stack of sl3 learners

metalearner_logistic_binomial() metalearner_linear() metalearner_linear_multivariate() metalearner_linear_multinomial()

Combine predictions from multiple learners

pack_predictions() unpack_predictions()

Pack multidimensional predictions into a vector (and unpack again)

pooled_hazard_task()

Generate A Pooled Hazards Task from a Failure Time (or Categorical) Task

predict_classes()

Predict Class from Predicted Probabilities

prediction_plot()

Plot predicted and true values for diganostic purposes

risk()

Risk Estimation

safe_dim()

dim that works for vectors too

sl3Options()

Querying/setting a single sl3 option

make_sl3_Task()

Define a Machine Learning Task

sl3_revere_Task

Revere (SplitSpecific) Task

subset_folds()

Make folds work on subset of data

undocumented_learner

Undocumented Learner

variable_type()

Specify variable type

write_learner_template()

Generate a file containing a template sl3 Learner