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**KNIME H2O Machine Learning Integration** version **4.2.0.v202006291607** by **KNIME AG, Zurich, Switzerland**

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

- 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.

- 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.

- 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.

- PCA model summary
- A table with the PCA model summary

- Table to H2O (79 %)
- H2O PCA Apply (7 %)
- H2O Generalized Low Rank Models (Missing Value Impute) (3 %)
- H2O Partitioning (3 %)
~~H2O Cross Validation Loop Start~~(2 %) Deprecated- Show all 8 recommendations

- H2O to Table (28 %)
- H2O PCA Compute (24 %)
- H2O PCA Apply (20 %)
- H2O Partitioning (7 %)
- H2O Statistics (4 %)
- Show all 18 recommendations

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

KNIME 4.2

A zipped version of the software site can be downloaded here. Read our FAQs to get instructions about how to install nodes from a zipped update site.

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