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H2O PCA Compute

KNIME H2O Machine Learning Integration version 4.4.0.v202106081612 by KNIME AG, Zurich, Switzerland

Compute a Principal Component Analysis (PCA) model using H2O. This model can later be used to reduce the dimensionality of a dataset using the PCA Apply node.


General Settings

Column selection
Select columns used for model training.
Ignore constant columns
Select to ignore constant columns.
Use static random seed
Select to use static seed for randomization.

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).
Rank of matrix approximation
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).
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.
Max runtime in secs
Maximum allowed runtime in seconds for model training (max_runtime_secs).

Input Ports

H2O Frame with the input data.

Output Ports

H2O Principal Component Analysis model.

Best Friends (Incoming)

Best Friends (Outgoing)



To use this node in KNIME, install KNIME H2O Machine Learning Integration from the following update site:


A zipped version of the software site can be downloaded here.

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