Defining A Task 


Define a Machine Learning Task 

Specify variable type 

Finding Learners 

List sl3 Learners 

sl3 Learners 

Harmonic Regression 

Univariate ARIMA Models 

BART Machine Learner 

Base Class for all sl3 Learners. 

Bidirectional Long shortterm memory Recurrent Neural Network (LSTM) 

Bound Predictions 

Wrapping Learner for Package Caret 

Fit/Predict a learner with Cross Validation 

CrossValidated Selector 

Discrete Bayesian Additive Regression Tree sampler 

Define interactions terms 

Density from Classification 

Density Estimation With Mean Model and Homoscedastic Errors 

Density Estimation With Mean Model and Homoscedastic Errors 

Earth  multivariate adaptive regression splines 

Exponential Smoothing 

Generalized Additive Models 

GBM  generalized boosted regression models 

Generalized Linear Models 

Computationally Efficient GLMs 

GLMs with Elastic Net Regularization 

Generalized Random Forests Learner 

Recurrent Neural Network with Gated Recurrent Unit (GRU) with Keras 

Grouped TimeSeries Forecasting 

h2o Model Definition 

Grid Search Models with h2o 

The Scalable Highly Adaptive Lasso 

Conditional Density Estimation with the Highly Adaptive LASSO 

Hierarchical TimeSeries Forecasting 

Classification from Binomial Regression 

Long shortterm memory Recurrent Neural Network (LSTM) 

Long shortterm memory Recurrent Neural Network (LSTM) with Keras 

Fitting Intercept Models 

Stratify univariable timeseries learners by timeseries 

Multivariate Learner 

FeedForward Neural Networks and Multinomial LogLinear Models 

Nonnegative Linear Least Squares 

Optimize Metalearner according to Loss Function using optim 

Principal Component Analysis and Regression 

Polyspline  multivariate adaptive polynomial spline regression (polymars) and polychotomous regression and multiple classification (polyclass) 

Classification from Pooled Hazards 

Random Forests 

Ranger  A Fast Implementation of Random Forests 

Learner that chains into a revere task 

Learner for Recursive Partitioning and Regression Trees. 

Univariate GARCH Models 

Augmented Covariate Screener 

Coefficient Magnitude Screener 

Correlation Screening Procedures 

Variable Importance Screener 

The Super Learner Algorithm 

Nonlinear Optimization via Augmented Lagrange 

Nonlinear Optimization via Augmented Lagrange 

Stratify learner fits by a single variable 

Learner with Covariate Subsetting 

Support Vector Machines 

Nonlinear Time Series Analysis 

Timespecific weighting of prediction losses 

xgboost: eXtreme Gradient Boosting 

Use SuperLearner Wrappers, Screeners, and Methods, in sl3 

Composing Learners 

Pipeline (chain) of learners. 

Learner Stacking 

Customize chaining for a learner 

Loss functions 


Loss Function Definitions 
Risk Estimation 

Metalearner functions 


Combine predictions from multiple learners 
Helpful for Defining Learners 

Generate a file containing a template 

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

Call with filtered argument list 

Estimate object size using serialization 

dim that works for vectors too 


Learner helpers 
Sample Datasets 

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

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

Bicycle sharing time series dataset 

Simulated data with continuous exposure 

Miscellaneous 

Querying/setting a single 

Index 

Estimate CrossValidated Risk of Super Learner 

Customize chaining for a learner 

Harmonic Regression 

Univariate ARIMA Models 

BART Machine Learner 

Base Class for all sl3 Learners. 

Bidirectional Long shortterm memory Recurrent Neural Network (LSTM) 

Bound Predictions 

Wrapping Learner for Package Caret 

Fit/Predict a learner with Cross Validation 

CrossValidated Selector 

Discrete Bayesian Additive Regression Tree sampler 

Define interactions terms 

Density from Classification 

Density Estimation With Mean Model and Homoscedastic Errors 

Density Estimation With Mean Model and Homoscedastic Errors 

Earth  multivariate adaptive regression splines 

Exponential Smoothing 

Generalized Additive Models 

GBM  generalized boosted regression models 

Generalized Linear Models 

Computationally Efficient GLMs 

GLMs with Elastic Net Regularization 

Generalized Random Forests Learner 

Recurrent Neural Network with Gated Recurrent Unit (GRU) with Keras 

Grouped TimeSeries Forecasting 

h2o Model Definition 

Grid Search Models with h2o 

The Scalable Highly Adaptive Lasso 

Conditional Density Estimation with the Highly Adaptive LASSO 

Hierarchical TimeSeries Forecasting 

Classification from Binomial Regression 

Long shortterm memory Recurrent Neural Network (LSTM) 

Long shortterm memory Recurrent Neural Network (LSTM) with Keras 

Fitting Intercept Models 

Stratify univariable timeseries learners by timeseries 

Multivariate Learner 

FeedForward Neural Networks and Multinomial LogLinear Models 

Nonnegative Linear Least Squares 

Optimize Metalearner according to Loss Function using optim 

Principal Component Analysis and Regression 

Polyspline  multivariate adaptive polynomial spline regression (polymars) and polychotomous regression and multiple classification (polyclass) 

Classification from Pooled Hazards 

Random Forests 

Ranger  A Fast Implementation of Random Forests 

Learner that chains into a revere task 

Learner for Recursive Partitioning and Regression Trees. 

Univariate GARCH Models 

Augmented Covariate Screener 

Coefficient Magnitude Screener 

Correlation Screening Procedures 

Variable Importance Screener 

The Super Learner Algorithm 

Nonlinear Optimization via Augmented Lagrange 

Nonlinear Optimization via Augmented Lagrange 

Stratify learner fits by a single variable 

Learner with Covariate Subsetting 

Support Vector Machines 

Nonlinear Time Series Analysis 

Timespecific weighting of prediction losses 

xgboost: eXtreme Gradient Boosting 

Pipeline (chain) of learners. 

Container Class for data.table Shared Between Tasks 

Learner Stacking 

Use SuperLearner Wrappers, Screeners, and Methods, in sl3 

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

Bicycle sharing time series dataset 

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

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

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. 

Crossvalidated Risk Estimation 


Helper functions to debug sl3 Learners 
Automatically Defined Metalearner 

Simulated data with continuous exposure 

Convert Factors to indicators 

Importance
Extract variable importance measures produced by


Variable Importance Plot 

Inverse CDF Sampling 


Learner helpers 
List sl3 Learners 


Loss Function Definitions 
Make a stack of sl3 learners 


Combine predictions from multiple learners 
Pack multidimensional predictions into a vector (and unpack again) 

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

Predict Class from Predicted Probabilities 

Plot predicted and true values for diganostic purposes 

Risk Estimation 

dim that works for vectors too 

Querying/setting a single 

Define a Machine Learning Task 

Revere (SplitSpecific) Task 

Make folds work on subset of data 

Undocumented Learner 

Specify variable type 

Generate a file containing a template 