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.

- 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.

- 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).

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To use this node in KNIME, install the extension KNIME H2O Machine Learning Integration from the below update site following our NodePit Product and Node Installation Guide:

v4.6

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