This value defaults to False.Įxport_checkpoints_dir: Specify a directory to which generated models will automatically be exported.įor PCA, this is dependent on the specified pca_method parameter:įor GramSVD, use fewer larger nodes for better performance. Impute_missing: Specifies whether to impute missing entries with the column mean value. This value is set to 0 (disabled) by default.
Max_runtime_secs: Maximum allowed runtime in seconds for model training. Score_each_iteration: (Optional) Specify whether to score during each iteration of the model training. This option is disabled by default.Ĭompute_metrics: Enable metrics computations on the training data. For PCA models, this option ignores the first factor level of each categorical column when expanding into indicator columns. By default, the first factor level is skipped. Use_all_factor_levels: Specify whether to use all factor levels in the possible set of predictors if you enable this option, sufficient regularization is required. This value defaults to -1 (time-based random number). The seed is consistent for each H2O instance so that you can create models with the same starting conditions in alternative configurations.
#R pca column generator#
Seed: Specify the random number generator (RNG) seed for algorithm components dependent on randomization. The value must be between 1 and 1e6 and the default is 1000. Max_iterations: Specify the number of training iterations. This can be a value from 1 to the minimum of (total number of rows, total number of columns) in the dataset. K: Specify the rank of matrix approximation. Jama: Eigenvalue decompositions for dense matrix using Java Matrix ( JAMA) Mtj_svd_densematrix: Singular-value decompositions for dense matrix using Matrix Toolkit Java ( MTJ) Mtj_evd_symmmatrix: Eigenvalue decompositions for symmetric matrix using Matrix Toolkit Java ( MTJ) (default) Mtj_evd_densematrix: Eigenvalue decompositions for dense matrix using Matrix Toolkit Java ( MTJ) Pca_impl: Specify the implementation to use for computing PCA (via SVD or EVD). GLRM: Fits a generalized low-rank model with L2 loss function and no regularization and solves for the SVD using local matrix algebra (experimental) Randomized: Uses randomized subspace iteration method Power: Computes the SVD using the power iteration method (experimental) GramSVD: Uses a distributed computation of the Gram matrix, followed by a local SVD using the JAMA package (default) Pca_method: Specify the algorithm to use for computing the principal components: Transform: Specify the transformation method for numeric columns in the training data: None, Standardize, Normalize, Demean, or Descale. Ignore_const_cols: Specify whether to ignore constant training columns, since no information can be gained from them. To change the selections for the hidden columns, use the Select Visible or Deselect Visible buttons. To only show columns with a specific percentage of missing values, specify the percentage in the Only show columns with more than 0% missing values field. To search for a specific column, type the column name in the Search field above the column list. To remove all columns from the list of ignored columns, click the None button.
To remove a column from the list of ignored columns, click the X next to the column name. To add all columns, click the All button. In Flow, click the checkbox next to a column name to add it to the list of columns excluded from the model. Ignored_columns: (Optional, Python and Flow only) Specify the column or columns to be excluded from the model. If x is missing, then all columns are used. X: Specify a vector containing the names or indices of the predictor variables to use when building the model. Validation_frame: (Optional) Specify the dataset to calculate validation metrics. NOTE: In Flow, if you click the Build a model button from the Parse cell, the training frame is entered automatically. Training_frame: (Required) Specify the dataset used to build the model. By default, H2O automatically generates a destination key.
Model_id: (Optional) Specify a custom name for the model to use as a reference.
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