Pipelines for Machine Learning and Super Learning

args_to_list | Get all args of parent call (both specified and defaults) as... |

bsds | Bicycle sharing time series dataset |

call_with_args | Call with filtered argument list |

cpp | Subset of growth data from the collaborative perinatal... |

cpp_1yr | Subset of growth data from the collaborative perinatal... |

Custom_chain | Customize chaining for a learner |

debug_helpers | Helper functions to debug sl3 Learners |

density_dat | Simulated data with continuous exposure |

drop_offsets_chain | Chain while dropping offsetes |

factors_to_indicators | Convert Factors to indicators |

keep_only_fun_args | Streamline Function Arguments |

learner_helpers | Learner helpers |

list_learners | List sl3 Learners |

loss_functions | Loss Function Definitions |

Lrnr_arima | Univariate ARIMA Models |

Lrnr_bartMachine | BART Machine Learner |

Lrnr_base | Base Class for all sl3 Learners. |

Lrnr_bilstm | Bidirectional Long short-term memory Recurrent Neural Network... |

Lrnr_condensier | Conditional Density Estimation |

Lrnr_cv | Fit/Predict a learner with Cross Validation |

Lrnr_dbarts | Discrete Bayesian Additive Regression Tree sampler |

Lrnr_define_interactions | Define interactions terms |

Lrnr_expSmooth | Exponential Smoothing |

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_h2o_glm | h2o Model Definition |

Lrnr_h2o_grid | Grid Search Models with h2o |

Lrnr_hal9001 | The Scalable Highly Adaptive LASSO |

Lrnr_HarmonicReg | Harmonic Regression |

Lrnr_independent_binomial | Classification from Binomial Regression |

Lrnr_lstm | Long short-term memory Recurrent Neural Network (LSTM) |

Lrnr_mean | Fitting Intercept 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_condensier_logisfitR6 | sl3 Learner wrapper for condensier |

Lrnr_randomForest | Random Forests |

Lrnr_ranger | Ranger - A Fast Implementation of Random Forests |

Lrnr_rpart | Learner for Recursive Partitioning and Regression Trees. |

Lrnr_rugarch | Univariate GARCH Models |

Lrnr_sl | SuperLearner 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_xgboost | xgboost: eXtreme Gradient Boosting |

make_learner_stack | Make a stack of sl3 learners |

metalearners | Combine predictions from multiple learners |

pack_predictions | Pack multidimensional predictions into a vector (and unpack... |

Pipeline | Pipeline (chain) of learners. |

predict_classes | Predict Class from Predicted Probabilities |

prediction_plot | Plot predicted and true values for diganostic purposes |

replace_add_user_args | Replace an argument in 'mainArgs' if it also appears in... |

risk | Risk Esimation |

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_Task | Define a Machine Learning Task |

Stack | Learner Stacking |

SuperLearner_interface | Use SuperLearner Wrappers, Screeners, and Methods, in sl3 |

true_obj_size | Estimate object size using serialization |

undocumented_learner | Undocumented Learner |

variable_type | Specify variable type |

write_learner_template | Generate a file containing a template 'sl3' Learner |

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