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**KNIME H2O Machine Learning Integration** version **4.3.0.v202012011122** by **KNIME AG, Zurich, Switzerland**

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).

- H2O Frame with the reconstructed input data.
- H2O Frame with the GLRM X matrix. The X matrix contains k principal components of the input data.

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

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

KNIME 4.3

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

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