Reconstruct the training data and impute missing values from the H2O GLRM model by computing the matrix product of X and Y, and transforming back to the original feature space by minimizing each column's loss function.
h2o.reconstruct(object, data, reverse_transform = FALSE)
object | An H2ODimReductionModel object that represents the model to be used for reconstruction. |
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data | An H2OFrame object representing the training data for the H2O GLRM model. Used to set the domain of each column in the reconstructed frame. |
reverse_transform | (Optional) A logical value indicating whether to reverse the transformation from model-building by re-scaling columns and adding back the offset to each column of the reconstructed frame. |
Returns an H2OFrame object containing the approximate reconstruction of the training data;
h2o.glrm
for making an H2ODimReductionModel.
# NOT RUN { library(h2o) h2o.init() iris_hf <- as.h2o(iris) iris_glrm <- h2o.glrm(training_frame = iris_hf, k = 4, transform = "STANDARDIZE", loss = "Quadratic", multi_loss = "Categorical", max_iterations = 1000) iris_rec <- h2o.reconstruct(iris_glrm, iris_hf, reverse_transform = TRUE) head(iris_rec) # }