0 ×

**KNIME H2O Machine Learning Integration** version **4.0.1.v201908131226** 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.

- 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).

- H2O PCA (57 %)
- Table to H2O (14 %)
- Table to H2O (14 %)
- Parameter Optimization Loop Start (14 %)

- H2O PCA Apply (50 %)
- Variable to Table Row (10 %)
- Model Writer (10 %)
~~H2O Cluster Assigner~~(10 %) Deprecated- H2O Model to MOJO (10 %)
~~H2O Predictor (Classification)~~(10 %) Deprecated- Show all 6 recommendations

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

KNIME 4.0

Do you have feedback, questions, comments about NodePit, want to support this platform, or want your own nodes or workflows listed here as well? Do you think, the search results could be improved or something is missing? Then please get in touch! Alternatively, you can send us an email to mail@nodepit.com, follow @NodePit on Twitter, or chat on Gitter!

Please note that this is only about NodePit. We do not provide general support for KNIME — please use the KNIME forums instead.