Generalized Linear Model (H2O)

Parameters

• family Family. Use binomial for classification with logistic regression, others are for regression problems.
  • AUTO: Automatic selection. Uses multinomial for polynominal, binomial for binominal and gaussian for numeric labels.
  • gaussian: The data must be numeric (real or integer).
  • binomial: The data must be binominal or polynominal with 2 levels/classes.
  • multinomial: The data must be polynominal with more than two levels/classes.
  • poisson: The data must be numeric and non-negative (integer).
  • gamma: The data must be numeric and continuous and positive (real or integer).
  • tweedie: The data must be numeric and continuous (real) and non-negative.
• solver Select the solver to use. IRLSM is fast on problems with a small number of predictors and for lambda-search with L1 penalty, while L_BFGS scales better for datasets with many columns. COORDINATE_DESCENT is IRLSM with the covariance updates version of cyclical coordinate descent in the innermost loop. COORDINATE_DESCENT_NAIVE is IRLSM with the naive updates version of cyclical coordinate descent in the innermost loop. COORDINATE_DESCENT_NAIVE and COORDINATE_DESCENT are currently experimental. Values:
  • AUTO
  • IRLSM
  • L_BFGS
  • COORDINATE_DESCENT (experimental)
  • COORDINATE_DESCENT_NAIVE (experimental)
• link The link function relates the linear predictor to the distribution function. The default is the canonical link for the specified family. Only available for gaussian, poisson and gamma families, because only one link type is possible for the others:
  • Family: binomial; Link: logit
  • Family: multinomial; Link: multinomial
  • Family: tweedie; Link: tweedie
  • family_default: Uses identity for gaussian, log for possion and inverse for gamma family.
  • identity: Possible family options: Gaussian, Poisson, Gamma
  • log: Possible family options: Gaussian, Poisson, Gamma
  • inverse: Possible family options: Gaussian, Gamma
• reproducible Makes model building reproducible. If set then maximum_number_of_threads parameter controls parallelism level of model building. If not set then parallelism level is defined by number of threads in General Preferences.
• maximum number of threads Controls parallelism level of model building.
• specify beta constraints If enabled, beta constraints for the regular attributes can be provided.
• use regularization Check this box if regularization should be used. For regularization, you can specify the lambda, alpha and the lambda search related parameters. If alpha or lambda is undefined (default), H2O will calculate default values for them based on the training data and the other parameters. If this parameter is set to false, lambda is set to 0.0 (means no regularization).
• lambda The lambda parameter controls the amount of regularization applied. If lambda is 0.0, no regularization is applied and the alpha parameter is ignored (you can set this by disabling the use regularization parameter). The default value for lambda is calculated by H2O using a heuristic based on the training data. Providing multiple lambda values via the advanced parameters triggers a search.
• lambda search A logical value indicating whether to conduct a search over the space of lambda values, starting from the max lambda, given lambda will be interpreted as the min lambda. Default is false.
• number of lambdas The number of lambda values when lambda search = true. 0 means no preference.
• lambda min ratio Smallest value for lambda as a fraction of lambda.max, the entry value, which is the smallest value for which all coefficients in the model are zero. If the number of observations is greater than the number of variables then default lambda_min_ratio = 0.0001; if the number of observations is less than the number of variables then default lambda_min_ratio = 0.01. Default is 0.0, which means no preference.
• early stopping Check this box if early stopping should be performed on the lambda search based on the stopping rounds and stopping tolerance parameters. The used stopping metric is always deviance.
• stopping rounds Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve for k:=stopping_rounds scoring events.
• stopping tolerance Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much).
• alpha The alpha parameter controls the distribution between the L1 (Lasso) and L2 (Ridge regression) penalties. A value of 1.0 for alpha represents Lasso, and an alpha value of 0.0 produces Ridge regression. Providing multiple alpha values via the advanced parameters triggers a search. Default is 0.0 for the L-BFGS solver, else 0.5.
• standardize Standardize numeric columns to have zero mean and unit variance
• non-negative coefficients Restrict coefficients (not intercept) to be non-negative.
• compute p-values Request p-values computation. P-values work only with IRLSM solver and no regularization. Intercept must also be added to the model. Moreover, non-negative coefficients and specify beta constraints parameters have to be set to false to compute p-values.
• remove collinear columns In case of linearly dependent columns remove some of the dependent columns. Works only if intercept is added to the model.
• add intercept Include constant term in the model.
• missing values handling Handling of missing values. Either Skip or MeanImputation.
  • Skip: Missing values are skipped.
  • MeanImputation: Missing values are replaced with the mean value.
• max iterations Maximum number of iterations. 0 means no limit.
• beta constraints Constraints for beta values. A row consists of the following values: Names
  • Attribute name: The name of the attribute.
  • Category: A value from the attribute's domain. Please take care to provide the exact value. Use more rows to specify constraints for multiple categories.
  Constraints
  • Lower bound: Lower bound of the beta.
  • Upper bound: Upper bound of the beta.
  • Beta given: Specifies the given solution in proximal operator interface. The proximal operator interface allows you to run the GLM with a proximal penalty on a distance from a specified given solution.
  • Beta start: Starting value of the beta.
• max runtime seconds Maximum allowed runtime in seconds for model training. Use 0 to disable.
• expert parameters These parameters are for fine tuning the algorithm. Usually the default values provide a decent model, but in some cases it may be useful to change them. Please use true/false values for boolean parameters and the exact attribute name for columns. Arrays can be provided by splitting the values with the comma (,) character. More information on the parameters can be found in the H2O documentation.
  • score_each_iteration: Whether to score during each iteration of model training. Type: boolean, Default: false
  • fold_assignment: Cross-validation fold assignment scheme, if fold_column is not specified. Options: AUTO, Random, Modulo, Stratified. Type: enumeration, Default: AUTO
  • fold_column: Column name with cross-validation fold index assignment per observation. Type: column, Default: no fold column
  • offset_column: Offset column name. Type: Column, Default: no offset column
  • max_confusion_matrix_size: Maximum size (# classes) for confusion matrices to be printed in the Logs. Type: integer, Default: 20
  • keep_cross_validation_predictions: Keep cross-validation model predictions. Type: boolean, Default: false
  • keep_cross_validation_fold_assignment: Keep cross-validation fold assignment. Type: boolean, Default: false
  • tweedie_variance_power: A numeric value specifying the power for the variance function when family = "tweedie". Type: real, Default: 0
  • tweedie_link_power: A numeric value specifying the power for the link function when family = "tweedie". Type: real, Default: 1
  • prior: A numeric specifying the prior probability of class 1 in the response when family = "binomial". Must be from (0,1) exclusive range or -1 (no prior). The default value is the observation frequency of class 1. Type: real Default: -1 (no prior)
  • beta_epsilon: A non-negative number specifying the magnitude of the maximum difference between the coefficient estimates from successive iterations. Defines the convergence criterion. Type: real, Default: 0.0001
  • objective_epsilon: Specify a threshold for convergence. If the objective value is less than this threshold, the model is converged. Type: real, Default: -1 (no threshold)
  • gradient_epsilon: (For L-BFGS only) Specify a threshold for convergence. If the objective value (using the L-infinity norm) is less than this threshold, the model is converged. Type: real, Default: 0.0001
  • max_active_predictors: Specify the maximum number of active predictors during computation. This value is used as a stopping criterium to prevent expensive model building with many predictors. Type: integer, Default: -1 (no limit)
  • obj_reg: Likelihood divider in objective value computation, Type: real, Default: 1/nobs
  • additional_alphas: Providing additional alphas triggers a search. Ignored if alpha is undefined.
  • additional_lambdas: Providing additional lambdas triggers a search. Ignored if lambda is undefined.
  • nfolds: Number of folds for cross-validation. Use 0 to turn off cross-validation. Type: integer, Default: 0