Apply a Generalized Low Rank Model (GLRM) using H2O to reconstruct missing values or identify important features in a dataset. Note that if the input data contains no missing values, the reconstructed data returned by this node will be the same as the input data.

- Column selection
- Select columns for the input data.
- Ignore constant columns
- Select to ignore constant columns.
- Use static random seed
- Select to use static seed for randomization.

- Transformation
- Specify the transformation method for the training data (None, Standardize, Normalize, Demean, or Descale). The default is None.
- Rank of matrix approximation
- Specify the rank of matrix approximation (required).
- Numerical loss function
- Specify the numeric loss function (Quadratic, Absolute, Huber, Poisson, or Periodic).
- Length of period
- Specify the length of the period (only enabled when the numerical loss function is set to Periodic).
- Categorical loss function
- Specify the categorical loss function (Categorical or Ordinal).
- Regularization function for X matrix
- Specify the regularization function for the X matrix (None, Quadratic, L2, L1, NonNegative, OneSparse, UnitOneSparse, or Simplex).
- Regularization function for Y matrix
- Specify the regularization function for the Y matrix (None, Quadratic, L2, L1, NonNegative, OneSparse, UnitOneSparse, or Simplex).
- Regularization weight on the X matrix
- Specify the regularization weight on the X matrix.
- Regularization weight on the Y matrix
- Specify the regularization weight on the Y matrix.
- Max number of iterations
- Specify the maximum number of training iterations. The maximum value is 1000000 (max_iterations).
- Max number of updates
- Specify the maximum number of updates.
- Initial step size
- Specify the initial step size.
- Min step size
- Specify the minimum step size. This value should be between 0 and the initial step size.
- Initialization mode
- Specify the initialization mode (Random, SVD, PlusPlus, or User).
- SVD method
- Specify the method for computing SVD during initialization (GramSVD, Power, Randomized). Note that Power and Randomized are currently experimental.
- Max runtime in seconds
- Specify the maximum allowed runtime in seconds for model training (max_runtime_secs).

- GLRM model summary
- A table with the GLRM model summary

- No links available

You want to see the source code for this node? Click the following button and we’ll use our super-powers to find it for you.

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:

v5.2

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

Deploy, schedule, execute, and monitor your KNIME workflows locally, in the cloud
or on-premises – with our brand new **NodePit Runner**.

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 botsin.space/@nodepit on Mastodon.

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