Man pages for tlverse/sl3
Pipelines for Machine Learning and Super Learning
| args_to_list | Get all arguments of parent call (both specified and... |
| bound | Truncates predictions to ensure loss function is bounded. |
| 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 |
| cv_helpers | Subset Tasks for CV THe functions use origami folds to subset... |
| cv_risk | Cross-validated Risk Estimation |
| cv_sl | Cross-validated Super Learner |
| debug_helpers | Helper functions to debug sl3 Learners |
| default_metalearner | Automatically Defined Metalearner |
| density_dat | Simulated data with continuous exposure |
| drop_offsets_chain | Chain while dropping offsets |
| factors_to_indicators | Convert Factors to indicators |
| importance | Importance Extract variable importance measures produced by... |
| importance_plot | Variable Importance Plot |
| impute | Impute missing values with the median/mode based on... |
| inverse_sample | Inverse CDF Sampling |
| 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 | bartMachine: Bayesian Additive Regression Trees (BART) |
| Lrnr_base | Base Class for all sl3 Learners |
| Lrnr_bayesglm | Bayesian Generalized Linear Models |
| Lrnr_bound | Bound Predictions |
| Lrnr_caret | Caret (Classification and Regression) Training |
| 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_glm_fast | Computationally Efficient Generalized Linear Model (GLM)... |
| Lrnr_glmnet | GLMs with Elastic Net Regularization |
| Lrnr_glm_semiparametric | Semiparametric Generalized Linear Models |
| Lrnr_glmtree | Generalized Linear Model Trees |
| Lrnr_grf | Generalized Random Forests Learner |
| Lrnr_grfcate | Generalized Random Forests for Conditional Average Treatment... |
| Lrnr_gru_keras | Recurrent Neural Network with Gated Recurrent Unit (GRU) with... |
| Lrnr_gts | Grouped Time-Series Forecasting |
| Lrnr_h2o_glm | h2o Model Definition |
| Lrnr_h2o_grid | 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... |
| 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... |
| 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... |
| 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 |
| 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. |
| pooled_hazard_task | Generate A Pooled Hazards Task from a Failure Time (or... |
| predict_classes | Predict Class from Predicted Probabilities |
| prediction_plot | Plot predicted and true values for diganostic purposes |
| process_data | Process Data |
| reduce_fit_test | Drop components from learner fits |
| replace_add_user_args | Replace an argument in 'mainArgs' if it also appears in... |
| risk | Risk Estimation |
| risk_functions | FACTORY RISK FUNCTION FOR ROCR PERFORMANCE MEASURES WITH... |
| 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_revere_Task | Revere (SplitSpecific) Task |
| sl3_Task | Define a Machine Learning Task |
| Stack | Learner Stacking |
| subset_dt_cols | Subset data.table columns |
| subset_folds | Make folds work on subset of data |
| 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 |
