H2O ModelSelection is used to build the best model with one predictor, two predictors, ... up to max_predictor_number specified in the algorithm parameters when mode=allsubsets. The best model is the one with the highest R2 value. When mode=maxr, the model returned is no longer guaranteed to have the best R2 value.
h2o.modelSelection(
x,
y,
training_frame,
model_id = NULL,
validation_frame = NULL,
nfolds = 0,
seed = -1,
fold_assignment = c("AUTO", "Random", "Modulo", "Stratified"),
fold_column = NULL,
ignore_const_cols = TRUE,
score_each_iteration = FALSE,
score_iteration_interval = 0,
offset_column = NULL,
weights_column = NULL,
family = c("AUTO", "gaussian", "binomial", "fractionalbinomial", "quasibinomial",
"poisson", "gamma", "tweedie", "negativebinomial"),
link = c("family_default", "identity", "logit", "log", "inverse", "tweedie", "ologit"),
tweedie_variance_power = 0,
tweedie_link_power = 0,
theta = 0,
solver = c("AUTO", "IRLSM", "L_BFGS", "COORDINATE_DESCENT_NAIVE", "COORDINATE_DESCENT",
"GRADIENT_DESCENT_LH", "GRADIENT_DESCENT_SQERR"),
alpha = NULL,
lambda = c(0),
lambda_search = FALSE,
early_stopping = FALSE,
nlambdas = 0,
standardize = TRUE,
missing_values_handling = c("MeanImputation", "Skip", "PlugValues"),
plug_values = NULL,
compute_p_values = FALSE,
remove_collinear_columns = FALSE,
intercept = TRUE,
non_negative = FALSE,
max_iterations = 0,
objective_epsilon = -1,
beta_epsilon = 1e-04,
gradient_epsilon = -1,
startval = NULL,
prior = 0,
cold_start = FALSE,
lambda_min_ratio = 0,
beta_constraints = NULL,
max_active_predictors = -1,
obj_reg = -1,
stopping_rounds = 0,
stopping_metric = c("AUTO", "deviance", "logloss", "MSE", "RMSE", "MAE", "RMSLE",
"AUC", "AUCPR", "lift_top_group", "misclassification", "mean_per_class_error",
"custom", "custom_increasing"),
stopping_tolerance = 0.001,
balance_classes = FALSE,
class_sampling_factors = NULL,
max_after_balance_size = 5,
max_runtime_secs = 0,
custom_metric_func = NULL,
nparallelism = 0,
max_predictor_number = 1,
min_predictor_number = 1,
mode = c("allsubsets", "maxr", "maxrsweep", "backward"),
build_glm_model = FALSE,
p_values_threshold = 0,
influence = c("dfbetas"),
multinode_mode = FALSE
)(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.
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.
Id of the training data frame.
Destination id for this model; auto-generated if not specified.
Id of the validation data frame.
Number of folds for K-fold cross-validation (0 to disable or >= 2). Defaults to 0.
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).
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.
Column with cross-validation fold index assignment per observation.
Logical. Ignore constant columns. Defaults to TRUE.
Logical. Whether to score during each iteration of model training. Defaults to FALSE.
Perform scoring for every score_iteration_interval iterations Defaults to 0.
Offset column. This will be added to the combination of columns before applying the link function.
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 per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more, due to the larger loss function pre-factor. If you set weight = 0 for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate prediction, remove all rows with weight == 0.
Family. For maxr/maxrsweep, only gaussian. For backward, ordinal and multinomial families are not supported Must be one of: "AUTO", "gaussian", "binomial", "fractionalbinomial", "quasibinomial", "poisson", "gamma", "tweedie", "negativebinomial". Defaults to AUTO.
Link function. Must be one of: "family_default", "identity", "logit", "log", "inverse", "tweedie", "ologit". Defaults to family_default.
Tweedie variance power Defaults to 0.
Tweedie link power Defaults to 0.
Theta Defaults to 0.
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. Must be one of: "AUTO", "IRLSM", "L_BFGS", "COORDINATE_DESCENT_NAIVE", "COORDINATE_DESCENT", "GRADIENT_DESCENT_LH", "GRADIENT_DESCENT_SQERR". Defaults to IRLSM.
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.
Regularization strength Defaults to c(0.0).
Logical. Use lambda search starting at lambda max, given lambda is then interpreted as lambda min
Defaults to FALSE.
Logical. Stop early when there is no more relative improvement on train or validation (if provided)
Defaults to FALSE.
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 0.
Logical. Standardize numeric columns to have zero mean and unit variance Defaults to TRUE.
Handling of missing values. Either MeanImputation, Skip or PlugValues. Must be one of: "MeanImputation", "Skip", "PlugValues". Defaults to MeanImputation.
Plug Values (a single row frame containing values that will be used to impute missing values of the training/validation frame, use with conjunction missing_values_handling = PlugValues)
Logical. Request p-values computation, p-values work only with IRLSM solver and no regularization
Defaults to FALSE.
Logical. In case of linearly dependent columns, remove some of the dependent columns Defaults to FALSE.
Logical. Include constant term in the model Defaults to TRUE.
Logical. Restrict coefficients (not intercept) to be non-negative Defaults to FALSE.
Maximum number of iterations Defaults to 0.
Converge if objective value changes less than this. Default (of -1.0) 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.
Converge if beta changes less (using L-infinity norm) than beta esilon, ONLY applies to IRLSM solver Defaults to 0.0001.
Converge if objective changes less (using L-infinity norm) than this, ONLY applies to L-BFGS solver. Default (of -1.0) 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.
double array to initialize fixed and random coefficients for HGLM, coefficients for GLM.
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 0.
Logical. Only applicable to multiple alpha/lambda values. If false, build the next model for next set
of alpha/lambda values starting from the values provided by current model. If true will start GLM model from
scratch. Defaults to FALSE.
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 0.
Beta constraints
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.
Likelihood divider in objective value computation, default (of -1.0) will set it to 1/nobs Defaults to -1.
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 (0 to disable) Defaults to 0.
Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anomaly_score for Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client. Must be one of: "AUTO", "deviance", "logloss", "MSE", "RMSE", "MAE", "RMSLE", "AUC", "AUCPR", "lift_top_group", "misclassification", "mean_per_class_error", "custom", "custom_increasing". Defaults to AUTO.
Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much) Defaults to 0.001.
Logical. Balance training data class counts via over/under-sampling (for imbalanced data). Defaults to
FALSE.
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.
Maximum relative size of the training data after balancing class counts (can be less than 1.0). Requires balance_classes. Defaults to 5.0.
Maximum allowed runtime in seconds for model training. Use 0 to disable. Defaults to 0.
Reference to custom evaluation function, format: `language:keyName=funcName`
number of models to build in parallel. Defaults to 0.0 which is adaptive to the system capability Defaults to 0.
Maximum number of predictors to be considered when building GLM models. Defaults to 1. Defaults to 1.
For mode = 'backward' only. Minimum number of predictors to be considered when building GLM models starting with all predictors to be included. Defaults to 1. Defaults to 1.
Mode: Used to choose model selection algorithms to use. Options include 'allsubsets' for all subsets, 'maxr' that uses sequential replacement and GLM to build all models, slow but works with cross-validation, validation frames for more robust results, 'maxrsweep' that uses sequential replacement and sweeping action, much faster than 'maxr', 'backward' for backward selection. Must be one of: "allsubsets", "maxr", "maxrsweep", "backward". Defaults to maxr.
Logical. For maxrsweep mode only. If true, will return full blown GLM models with the desired
predictorsubsets. If false, only the predictor subsets, predictor coefficients are returned. This is
forspeeding up the model selection process. The users can choose to build the GLM models themselvesby using
the predictor subsets themselves. Defaults to false. Defaults to FALSE.
For mode='backward' only. If specified, will stop the model building process when all coefficientsp-values drop below this threshold Defaults to 0.
If set to dfbetas will calculate the difference in beta when a datarow is included and excluded in the dataset. Must be one of: "dfbetas".
Logical. For maxrsweep only. If enabled, will attempt to perform sweeping action using multiple nodes
in the cluster. Defaults to false. Defaults to FALSE.
if (FALSE) {
library(h2o)
h2o.init()
# Run ModelSelection of VOL ~ all predictors
prostate_path <- system.file("extdata", "prostate.csv", package = "h2o")
prostate <- h2o.uploadFile(path = prostate_path)
prostate$CAPSULE <- as.factor(prostate$CAPSULE)
model <- h2o.modelSelection(y="VOL", x=c("RACE","AGE","RACE","DPROS"), training_frame=prostate)
}
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