This node applies a Principal Component Analysis (PCA) model using H2O.


General Settings

Dimensions to reduce to
Specify the rank of matrix approximation (the dimension of the principal components). Note that the dimension cannot be larger than the dimension of the input data (k).
Column selection
Select columns used for model training.
Ignore constant columns
Select to ignore constant columns.

Algorithm Settings

Transformation method
Specify the transformation method for the training data (None, Standardize, Normalize, Demean, or Descale) (transform).
PCA method
Specify the algorithm to use for computing the principal components (pca_method).
Number of max iterations
Specify the number of training iterations (max_iterations).
Use all factor levels
Specify whether to use all factor levels in the possible set of predictors (use_all_factor_levels).
Impute missing values
Specify whether to impute missing entries with the column mean value (impute_missing). Note that if this option is set to false, any rows with missing values will be removed.

Advanced Settings

Max runtime in seconds
Maximum allowed runtime in seconds for model training (max_runtime_secs).
Use static random seed
Select to use static seed for randomization.

Input Ports

H2O Frame with input data.

Output Ports

H2O Frame with the principal components of the training data.


PCA model summary
A table with the PCA model summary


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