Fits a generalized linear model, specified by a response variable, a set of predictors, and a description of the error distribution.

```
h2o.glm(x, y, training_frame, model_id = NULL, validation_frame = NULL,
nfolds = 0, seed = -1, keep_cross_validation_predictions = FALSE,
keep_cross_validation_fold_assignment = FALSE, fold_assignment = c("AUTO",
"Random", "Modulo", "Stratified"), fold_column = NULL,
ignore_const_cols = TRUE, score_each_iteration = FALSE,
offset_column = NULL, weights_column = NULL, family = c("gaussian",
"binomial", "quasibinomial", "multinomial", "poisson", "gamma", "tweedie"),
tweedie_variance_power = 0, tweedie_link_power = 1, solver = c("AUTO",
"IRLSM", "L_BFGS", "COORDINATE_DESCENT_NAIVE", "COORDINATE_DESCENT"),
alpha = NULL, lambda = NULL, lambda_search = FALSE,
early_stopping = TRUE, nlambdas = -1, standardize = TRUE,
missing_values_handling = c("MeanImputation", "Skip"),
compute_p_values = FALSE, remove_collinear_columns = FALSE,
intercept = TRUE, non_negative = FALSE, max_iterations = -1,
objective_epsilon = -1, beta_epsilon = 1e-04, gradient_epsilon = -1,
link = c("family_default", "identity", "logit", "log", "inverse",
"tweedie"), prior = -1, lambda_min_ratio = -1, beta_constraints = NULL,
max_active_predictors = -1, interactions = NULL,
balance_classes = FALSE, class_sampling_factors = NULL,
max_after_balance_size = 5, max_hit_ratio_k = 0, max_runtime_secs = 0)
```

x

A vector containing the names or indices of the predictor variables to use in building the model. If x is missing,then all columns except y are used.

y

The name of the response variable in the model.If the data does not contain a header, this is the first column index, and increasing from left to right. (The response must be either an integer or a categorical variable).

training_frame

Id of the training data frame (Not required, to allow initial validation of model parameters).

model_id

Destination id for this model; auto-generated if not specified.

validation_frame

Id of the validation data frame.

nfolds

Number of folds for N-fold cross-validation (0 to disable or >= 2). Defaults to 0.

seed

Seed for random numbers (affects certain parts of the algo that are stochastic and those might or might not be enabled by default) Defaults to -1 (time-based random number).

keep_cross_validation_predictions

`Logical`

. Whether to keep the predictions of the cross-validation models. Defaults to FALSE.

keep_cross_validation_fold_assignment

`Logical`

. Whether to keep the cross-validation fold assignment. Defaults to FALSE.

fold_assignment

Cross-validation fold assignment scheme, if fold_column is not specified. The 'Stratified' option will stratify the folds based on the response variable, for classification problems. Must be one of: "AUTO", "Random", "Modulo", "Stratified". Defaults to AUTO.

fold_column

Column with cross-validation fold index assignment per observation.

ignore_const_cols

`Logical`

. Ignore constant columns. Defaults to TRUE.

score_each_iteration

`Logical`

. Whether to score during each iteration of model training. Defaults to FALSE.

offset_column

Offset column. This will be added to the combination of columns before applying the link function.

weights_column

Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed.

family

Family. Use binomial for classification with logistic regression, others are for regression problems. Must be one of: "gaussian", "binomial", "quasibinomial", "multinomial", "poisson", "gamma", "tweedie". Defaults to gaussian.

tweedie_variance_power

Tweedie variance power Defaults to 0.

tweedie_link_power

Tweedie link power Defaults to 1.

solver

AUTO will set the solver based on given data and the other parameters. IRLSM is fast on on problems with small number of predictors and for lambda-search with L1 penalty, L_BFGS scales better for datasets with many columns. Coordinate descent is experimental (beta). Must be one of: "AUTO", "IRLSM", "L_BFGS", "COORDINATE_DESCENT_NAIVE", "COORDINATE_DESCENT". Defaults to AUTO.

alpha

Distribution of regularization between the L1 (Lasso) and L2 (Ridge) penalties. A value of 1 for alpha represents Lasso regression, a value of 0 produces Ridge regression, and anything in between specifies the amount of mixing between the two. Default value of alpha is 0 when SOLVER = 'L-BFGS'; 0.5 otherwise.

lambda

Regularization strength

lambda_search

`Logical`

. Use lambda search starting at lambda max, given lambda is then interpreted as lambda min
Defaults to FALSE.

early_stopping

`Logical`

. Stop early when there is no more relative improvement on train or validation (if provided)
Defaults to TRUE.

nlambdas

Number of lambdas to be used in a search. Default indicates: If alpha is zero, with lambda search set to True, the value of nlamdas is set to 30 (fewer lambdas are needed for ridge regression) otherwise it is set to 100. Defaults to -1.

standardize

`Logical`

. Standardize numeric columns to have zero mean and unit variance Defaults to TRUE.

missing_values_handling

Handling of missing values. Either MeanImputation or Skip. Must be one of: "MeanImputation", "Skip". Defaults to MeanImputation.

compute_p_values

`Logical`

. Request p-values computation, p-values work only with IRLSM solver and no regularization
Defaults to FALSE.

remove_collinear_columns

`Logical`

. In case of linearly dependent columns, remove some of the dependent columns Defaults to FALSE.

intercept

`Logical`

. Include constant term in the model Defaults to TRUE.

non_negative

`Logical`

. Restrict coefficients (not intercept) to be non-negative Defaults to FALSE.

max_iterations

Maximum number of iterations Defaults to -1.

objective_epsilon

Converge if objective value changes less than this. Default indicates: If lambda_search is set to True the value of objective_epsilon is set to .0001. If the lambda_search is set to False and lambda is equal to zero, the value of objective_epsilon is set to .000001, for any other value of lambda the default value of objective_epsilon is set to .0001. Defaults to -1.

beta_epsilon

Converge if beta changes less (using L-infinity norm) than beta esilon, ONLY applies to IRLSM solver Defaults to 0.0001.

gradient_epsilon

Converge if objective changes less (using L-infinity norm) than this, ONLY applies to L-BFGS solver. Default indicates: If lambda_search is set to False and lambda is equal to zero, the default value of gradient_epsilon is equal to .000001, otherwise the default value is .0001. If lambda_search is set to True, the conditional values above are 1E-8 and 1E-6 respectively. Defaults to -1.

link

Must be one of: "family_default", "identity", "logit", "log", "inverse", "tweedie". Defaults to family_default.

prior

Prior probability for y==1. To be used only for logistic regression iff the data has been sampled and the mean of response does not reflect reality. Defaults to -1.

lambda_min_ratio

Minimum lambda used in lambda search, specified as a ratio of lambda_max. Default indicates: if the number of observations is greater than the number of variables then lambda_min_ratio is set to 0.0001; if the number of observations is less than the number of variables then lambda_min_ratio is set to 0.01. Defaults to -1.

beta_constraints

Beta constraints

max_active_predictors

Maximum number of active predictors during computation. Use as a stopping criterion to prevent expensive model building with many predictors. Default indicates: If the IRLSM solver is used, the value of max_active_predictors is set to 7000 otherwise it is set to 100000000. Defaults to -1.

interactions

A list of predictor column indices to interact. All pairwise combinations will be computed for the list.

balance_classes

`Logical`

. Balance training data class counts via over/under-sampling (for imbalanced data). Defaults to
FALSE.

class_sampling_factors

Desired over/under-sampling ratios per class (in lexicographic order). If not specified, sampling factors will be automatically computed to obtain class balance during training. Requires balance_classes.

max_after_balance_size

Maximum relative size of the training data after balancing class counts (can be less than 1.0). Requires balance_classes. Defaults to 5.0.

max_hit_ratio_k

Maximum number (top K) of predictions to use for hit ratio computation (for multi-class only, 0 to disable) Defaults to 0.

max_runtime_secs

Maximum allowed runtime in seconds for model training. Use 0 to disable. Defaults to 0.

A subclass of ```
is returned. The specific subclass depends on the machine
learning task at hand (if it's binomial classification, then an
```

```
is
returned, if it's regression then a
```

```
is returned). The default print-
out of the models is shown, but further GLM-specifc information can be queried out of the object. To access
these various items, please refer to the seealso section below. Upon completion of the GLM, the resulting
object has coefficients, normalized coefficients, residual/null deviance, aic, and a host of model metrics
including MSE, AUC (for logistic regression), degrees of freedom, and confusion matrices. Please refer to the
more in-depth GLM documentation available here:
https://h2o-release.s3.amazonaws.com/h2o-dev/rel-shannon/2/docs-website/h2o-docs/index.html#Data+Science+Algorithms-GLM
```

`predict.H2OModel`

for prediction, `h2o.mse`

, `h2o.auc`

,
`h2o.confusionMatrix`

, `h2o.performance`

, `h2o.giniCoef`

,
`h2o.logloss`

, `h2o.varimp`

, `h2o.scoreHistory`

h2o.init() # Run GLM of CAPSULE ~ AGE + RACE + PSA + DCAPS prostatePath = system.file("extdata", "prostate.csv", package = "h2o") prostate.hex = h2o.importFile(path = prostatePath, destination_frame = "prostate.hex") h2o.glm(y = "CAPSULE", x = c("AGE","RACE","PSA","DCAPS"), training_frame = prostate.hex, family = "binomial", nfolds = 0, alpha = 0.5, lambda_search = FALSE) # Run GLM of VOL ~ CAPSULE + AGE + RACE + PSA + GLEASON myX = setdiff(colnames(prostate.hex), c("ID", "DPROS", "DCAPS", "VOL")) h2o.glm(y = "VOL", x = myX, training_frame = prostate.hex, family = "gaussian", nfolds = 0, alpha = 0.1, lambda_search = FALSE) # GLM variable importance # Also see: # https://github.com/h2oai/h2o/blob/master/R/tests/testdir_demos/runit_demo_VI_all_algos.R data.hex = h2o.importFile( path = "https://s3.amazonaws.com/h2o-public-test-data/smalldata/demos/bank-additional-full.csv", destination_frame = "data.hex") myX = 1:20 myY="y" my.glm = h2o.glm(x=myX, y=myY, training_frame=data.hex, family="binomial", standardize=TRUE, lambda_search=TRUE)