e: Embeddings
In cito: Building and Training Neural Networks
e R Documentation
Embeddings
Description
Can be used for categorical variables, a more efficient alternative to one-hot encoding
Usage
e(dim = 1L, weights = NULL, train = TRUE, lambda = 0, alpha = 1)
Arguments
dim
integer, embedding dimension
weights
matrix, to use custom embedding matrices
train
logical, should the embeddings be trained or not
lambda
regularization strength on the embeddings
alpha
mix between L1 and L2 regularization
cito documentation built on May 29, 2024, 1:28 a.m.
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| e | R Documentation |
Embeddings
Description
Can be used for categorical variables, a more efficient alternative to one-hot encoding
Usage
e(dim = 1L, weights = NULL, train = TRUE, lambda = 0, alpha = 1)
Arguments
dim |
integer, embedding dimension |
weights |
matrix, to use custom embedding matrices |
train |
logical, should the embeddings be trained or not |
lambda |
regularization strength on the embeddings |
alpha |
mix between L1 and L2 regularization |
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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