Description Usage Format Value Fields See Also

This learner supports long short-term memory recurrent neural network algorithm. In order to use this learner, you will need keras Python module 2.0.0 or higher. Note that all preprocessing, such as differencing and seasonal effects for time series, should be addressed before using this learner.

1 |

`R6Class`

object.

`Lrnr_base`

object with methods for training and prediction

`units`

Positive integer, dimensionality of the output space.

`loss`

Name of a loss function used.

`optimizer`

name of optimizer, or optimizer object.

`batch_size`

Number of samples per gradient update.

`epochs`

Number of epochs to train the model.

`window`

Size of the sliding window input.

`activation`

The activation function to use.

`dense`

regular, densely-connected NN layer. Default is 1.

`dropout`

float between 0 and 1. Fraction of the input units to drop.

Other Learners: `Custom_chain`

,
`Lrnr_HarmonicReg`

, `Lrnr_arima`

,
`Lrnr_bartMachine`

, `Lrnr_base`

,
`Lrnr_bilstm`

, `Lrnr_condensier`

,
`Lrnr_cv`

, `Lrnr_dbarts`

,
`Lrnr_define_interactions`

,
`Lrnr_expSmooth`

,
`Lrnr_glm_fast`

, `Lrnr_glmnet`

,
`Lrnr_glm`

, `Lrnr_grf`

,
`Lrnr_h2o_grid`

, `Lrnr_hal9001`

,
`Lrnr_independent_binomial`

,
`Lrnr_mean`

, `Lrnr_nnls`

,
`Lrnr_optim`

, `Lrnr_pca`

,
`Lrnr_pkg_SuperLearner`

,
`Lrnr_randomForest`

,
`Lrnr_ranger`

, `Lrnr_rpart`

,
`Lrnr_rugarch`

, `Lrnr_sl`

,
`Lrnr_solnp_density`

,
`Lrnr_solnp`

, `Lrnr_stratified`

,
`Lrnr_subset_covariates`

,
`Lrnr_svm`

, `Lrnr_tsDyn`

,
`Lrnr_xgboost`

, `Pipeline`

,
`Stack`

, `define_h2o_X`

,
`undocumented_learner`

jeremyrcoyle/sl3 documentation built on Nov. 13, 2018, 3:23 p.m.

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