Lrnr_nnet: Feed-Forward Neural Networks and Multinomial Log-Linear...

In tlverse/sl3: Pipelines for Machine Learning and Super Learning

Feed-Forward Neural Networks and Multinomial Log-Linear Models

Description

This learner provides feed-forward neural networks with a single hidden layer, and for multinomial log-linear models.

Format

R6Class object.

Value

Learner object with methods for both training and prediction. See Lrnr_base for documentation on learners.

Parameters

formula

A formula of the form class ~ x1 + x2 + ...

weights

(case) weights for each example – if missing defaults to 1

size

number of units in the hidden layer. Can be zero if there are skip-layer units.

entropy

switch for entropy (= maximum conditional likelihood) fitting. Default by least-squares.

decay

parameter for weight decay. Default 0.

maxit

maximum number of iterations. Default 100.

linout

switch for linear output units. Default logistic output units.

...

Other parameters passed to nnet.

Common Parameters

Individual learners have their own sets of parameters. Below is a list of shared parameters, implemented by Lrnr_base, and shared by all learners.

covariates

A character vector of covariates. The learner will use this to subset the covariates for any specified task

outcome_type

A variable_type object used to control the outcome_type used by the learner. Overrides the task outcome_type if specified

...

All other parameters should be handled by the invidual learner classes. See the documentation for the learner class you're instantiating

See Also

Other Learners: Custom_chain, Lrnr_HarmonicReg, Lrnr_arima, Lrnr_bartMachine, Lrnr_base, Lrnr_bayesglm, Lrnr_caret, Lrnr_cv_selector, Lrnr_cv, Lrnr_dbarts, Lrnr_define_interactions, Lrnr_density_discretize, Lrnr_density_hse, Lrnr_density_semiparametric, Lrnr_earth, Lrnr_expSmooth, Lrnr_gam, Lrnr_ga, Lrnr_gbm, Lrnr_glm_fast, Lrnr_glm_semiparametric, Lrnr_glmnet, Lrnr_glmtree, Lrnr_glm, Lrnr_grfcate, Lrnr_grf, Lrnr_gru_keras, Lrnr_gts, Lrnr_h2o_grid, Lrnr_hal9001, Lrnr_haldensify, Lrnr_hts, Lrnr_independent_binomial, Lrnr_lightgbm, Lrnr_lstm_keras, Lrnr_mean, Lrnr_multiple_ts, Lrnr_multivariate, Lrnr_nnls, Lrnr_optim, Lrnr_pca, Lrnr_pkg_SuperLearner, Lrnr_polspline, Lrnr_pooled_hazards, Lrnr_randomForest, Lrnr_ranger, Lrnr_revere_task, Lrnr_rpart, Lrnr_rugarch, Lrnr_screener_augment, Lrnr_screener_coefs, Lrnr_screener_correlation, Lrnr_screener_importance, Lrnr_sl, Lrnr_solnp_density, Lrnr_solnp, Lrnr_stratified, Lrnr_subset_covariates, Lrnr_svm, Lrnr_tsDyn, Lrnr_ts_weights, Lrnr_xgboost, Pipeline, Stack, define_h2o_X(), undocumented_learner

Examples

set.seed(123)

# load example data
data(cpp_imputed)
covars <- c("bmi", "parity", "mage", "sexn")
outcome <- "haz"

# create sl3 task
task <- sl3_Task$new(cpp_imputed, covariates = covars, outcome = outcome)

# train neural networks and make predictions
lrnr_nnet <- Lrnr_nnet$new(linout = TRUE, size = 10, maxit = 1000)
fit <- lrnr_nnet$train(task)
preds <- fit$predict(task)

tlverse/sl3 documentation built on Nov. 18, 2024, 12:46 a.m.