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_models = TRUE, 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", "ordinal", "multinomial", "poisson", "gamma", "tweedie", "negativebinomial"), tweedie_variance_power = 0, tweedie_link_power = 1, theta = 1e10, solver = c("AUTO", "IRLSM", "L_BFGS", "COORDINATE_DESCENT_NAIVE", "COORDINATE_DESCENT", "GRADIENT_DESCENT_LH", "GRADIENT_DESCENT_SQERR"), 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 = 1e04, gradient_epsilon = 1, link = c("family_default", "identity", "logit", "log", "inverse", "tweedie", "ologit", "oprobit", "ologlog"), prior = 1, lambda_min_ratio = 1, beta_constraints = NULL, max_active_predictors = 1, interactions = NULL, interaction_pairs = NULL, obj_reg = 1, export_checkpoints_dir = NULL, balance_classes = FALSE, class_sampling_factors = NULL, max_after_balance_size = 5, max_hit_ratio_k = 0, max_runtime_secs = 0, custom_metric_func = NULL)
x  (Optional) 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 or column index of the response variable in the data. The response must be either a numeric or a categorical/factor variable. If the response is numeric, then a regression model will be trained, otherwise it will train a classification model. 
training_frame  Id of the training data frame. 
model_id  Destination id for this model; autogenerated if not specified. 
validation_frame  Id of the validation data frame. 
nfolds  Number of folds for Kfold crossvalidation (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 (timebased random number). 
keep_cross_validation_models 

keep_cross_validation_predictions 

keep_cross_validation_fold_assignment 

fold_assignment  Crossvalidation 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 crossvalidation fold index assignment per observation. 
ignore_const_cols 

score_each_iteration 

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. Note: Weights are perrow observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but noninteger values are supported as well. During training, rows with higher weights matter more, due to the larger loss function prefactor. 
family  Family. Use binomial for classification with logistic regression, others are for regression problems. Must be one of: "gaussian", "binomial", "quasibinomial", "ordinal", "multinomial", "poisson", "gamma", "tweedie", "negativebinomial". Defaults to gaussian. 
tweedie_variance_power  Tweedie variance power Defaults to 0. 
tweedie_link_power  Tweedie link power Defaults to 1. 
theta  Theta Defaults to 1e10. 
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 lambdasearch with L1 penalty, L_BFGS scales better for datasets with many columns. Must be one of: "AUTO", "IRLSM", "L_BFGS", "COORDINATE_DESCENT_NAIVE", "COORDINATE_DESCENT", "GRADIENT_DESCENT_LH", "GRADIENT_DESCENT_SQERR". 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 = 'LBFGS'; 0.5 otherwise. 
lambda  Regularization strength 
lambda_search 

early_stopping 

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 

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

remove_collinear_columns 

intercept 

non_negative 

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 Linfinity norm) than beta esilon, ONLY applies to IRLSM solver Defaults to 0.0001. 
gradient_epsilon  Converge if objective changes less (using Linfinity norm) than this, ONLY applies to LBFGS 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 1E8 and 1E6 respectively. Defaults to 1. 
link  Must be one of: "family_default", "identity", "logit", "log", "inverse", "tweedie", "ologit", "oprobit", "ologlog". 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 (the smallest lambda that drives all coefficients to zero). 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 5000 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. 
interaction_pairs  A list of pairwise (first order) column interactions. 
obj_reg  Likelihood divider in objective value computation, default is 1/nobs Defaults to 1. 
export_checkpoints_dir  Automatically export generated models to this directory. 
balance_classes 

class_sampling_factors  Desired over/undersampling 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 multiclass 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. 
custom_metric_func  Reference to custom evaluation function, format: `language:keyName=funcName` 
A subclass of H2OModel
is returned. The specific subclass depends on the machine
learning task at hand (if it's binomial classification, then an H2OBinomialModel
is
returned, if it's regression then a H2ORegressionModel
is returned). The default print
out of the models is shown, but further GLMspecifc 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 indepth GLM documentation available here:
https://h2orelease.s3.amazonaws.com/h2odev/relshannon/2/docswebsite/h2odocs/index.html#Data+Science+AlgorithmsGLM
predict.H2OModel
for prediction, h2o.mse
, h2o.auc
,
h2o.confusionMatrix
, h2o.performance
, h2o.giniCoef
,
h2o.logloss
, h2o.varimp
, h2o.scoreHistory
# NOT RUN { h2o.init() # Run GLM of CAPSULE ~ AGE + RACE + PSA + DCAPS prostate_path = system.file("extdata", "prostate.csv", package = "h2o") prostate = h2o.importFile(path = prostate_path) h2o.glm(y = "CAPSULE", x = c("AGE","RACE","PSA","DCAPS"), training_frame = prostate, family = "binomial", nfolds = 0, alpha = 0.5, lambda_search = FALSE) # Run GLM of VOL ~ CAPSULE + AGE + RACE + PSA + GLEASON predictors = setdiff(colnames(prostate), c("ID", "DPROS", "DCAPS", "VOL")) h2o.glm(y = "VOL", x = predictors, training_frame = prostate, 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 bank = h2o.importFile( path = "https://s3.amazonaws.com/h2opublictestdata/smalldata/demos/bankadditionalfull.csv") predictors = 1:20 target="y" glm = h2o.glm(x=predictors, y=target, training_frame=bank, family="binomial", standardize=TRUE, lambda_search=TRUE) h2o.std_coef_plot(glm, num_of_features = 20) # }