Learns a Generalized Linear Model (GLM) classification model using H2O .

- Target column selection
- Select target column. Must be nominal for classification problems.
- Column selection
- Select columns used for model training.
- Ignore constant columns
- Select to ignore constant columns.
- Use static random seed
- Select to use static seed for randomization.

- Solver
- Specify the solver to use (AUTO, IRLSM, L_BFGS, COORDINATE_DESCENT_NAIVE, or COORDINATE_DESCENT). 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 (solver) .
- Family
- Specify the model type (family) .
- Link
- Specify a link function (Identity, Family_Default, Logit, Log, Inverse, or Tweedie) (link) .
- Alpha
- Specify the regularization distribution between L1 and L2 (alpha) .
- Lambda
- Specify the regularization strength (lambda) .
- Enable Lambda search
- Specify whether to enable lambda search, starting with lambda max. If you also specify a value for lambda_min_ratio, then this value is interpreted as lambda min. If you do not specify a value for lambda_min_ratio, then GLM will calculate the minimum lambda (lambda_search) .
- Number of Lambdas
- (Applicable only if lambda_search is enabled) Specify the number of lambdas to use in the search. The default is 100. (nlambdas) .
- Lambda minimum ratio
- Specify the minimum lambda to use for lambda search (specified as a ratio of lambda_max) (lambda_min_ratio) .
- Beta epsilon
- Specify the beta epsilon value. If the L1 normalization of the current beta change is below this threshold, consider using convergence (beta_epsilon) .
- Objective epsilon
- Specify a threshold for convergence. If the objective value is less than this threshold, the model is converged (objective_epsilon) .
- 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 (gradient_epsilon) .
- Non negative?
- Specify whether to force coefficients to have non-negative values (non_negative) .
- Max iterations
- Specify the number of training iterations (max_iterations) .
- Include a constant term in the model
- Specify whether to include a constant term in the model. This option is enabled by default (intercept) .
- Maximum 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 (max_active_predictors) .
- Compute P values
- Request computation of p-values. Only applicable with no penalty (lambda = 0 and no beta constraints). Setting remove_collinear_columns is recommended. H2O will return an error if p-values are requested and there are collinear columns and remove_collinear_columns flag is not enabled (compute_p_values) .
- Remove collinear columns
- Specify whether to automatically remove collinear columns during model-building. When enabled, collinear columns will be dropped from the model and will have 0 coefficient in the returned model. This can only be set if there is no regularization (lambda=0) (remove_colinear_columns) .
- Standardize numeric columns
- Specify whether to standardize the numeric columns to have a mean of zero and unit variance (recommended). (standardize) .
- Missing values handling
- Specify how to handle missing values (Skip or MeanImputation) (missing_values_handling) .

- Weight column selection
- Select a column to use for the observation weights, which are used for bias correction (weights_column) .
- Max Runtime?
- Maximum allowed runtime in seconds for model training (max_runtime_secs) .
- Early Stopping
- Select to activate early stopping.
- Stopping metric
- Specify the metric to use for early stopping (stopping_metric) .
- Stopping tolerance
- Specify the relative tolerance for the metric-based stopping to stop training if the improvement is less than this value (stopping_tolerance) .
- Number of last seen rows for moving average
- Stops training when the option selected for stopping_metric doesn’t improve for the specified number of training rounds, based on a simple moving average. To disable this feature, specify 0. The metric is computed on the validation data (if provided); otherwise, training data is used (stopping_rounds) .
- Size of validation set (in %)
- Specify the size of the validation data-set used to evaluate early stopping criteria.

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