0 ×

H2O Generalized Low Rank Models (Missing Value Impute)

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.

Options

General Settings

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.

Algorithm Settings

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).

Input Ports

Icon
H2O Frame with input data.

Output Ports

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

Views

GLRM model summary
A table with the GLRM model summary

Best Friends (Incoming)

Best Friends (Outgoing)

Installation

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.

You don't know what to do with this link? Read our NodePit Product and Node Installation Guide that explains you in detail how to install nodes to your KNIME Analytics Platform.

Wait a sec! You want to explore and install nodes even faster? We highly recommend our NodePit for KNIME extension for your KNIME Analytics Platform. Browse NodePit from within KNIME, install nodes with just one click and share your workflows with NodePit Space.

Developers

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.