We should add a page on RuleFit to the Supervised algorithms section in the User Guide: http://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science.html#supervised
The new content:
Rulefit algorithm combines tree ensembles and linear models to take advantage of both methods: a tree ensemble accuracy and a linear model interpretability.
The general algorithm fits a tree ensebmle to the data, builds a rule ensemble by traversing each tree, evaluates the rules on the data to build a rule feature set and fits a sparse linear model (LASSO) to the rule feature set joined with the original feature set.
model_id: (Optional) Specify a custom name for the model to use as a reference. By default, H2O automatically generates a destination key.
training_frame: (Required) Specify the dataset used to build the model. NOTE: In Flow, if you click the Build a model button from the Parse cell, the training frame is entered automatically.
validation_frame: (Optional) Specify the dataset used to evaluate the accuracy of the model.
seed: Specify the random number generator (RNG) seed for algorithm components dependent on randomization. The seed is consistent for each H2O instance so that you can create models with the same starting conditions in alternative configurations.
y: (Required) Specify the column to use as the dependent variable.
For a regression model, this column must be numeric (Real or Int).
For a classification model, this column must be categorical (Enum or String). If the family is Binomial, the dataset cannot contain more than two levels.
x: Specify a vector containing the names or indices of the predictor variables to use when building the model. If x is missing, then all columns except y are used.
algorithm: The algorithm to use to fit a tree ensemble. Must be one of: "AUTO", "DRF", "GBM". Defaults to DRF.
min_rule_length: Specify minimal depth of trees to be fit. Defaults to 1.
max_rule_length: Specify maximal depth of trees to be fit. Defaults to 10.
max_num_rules: The maximum number of rules to return. Defaults to -1, which means the number of rules is selected by diminishing returns in model deviance.
model_type: Specify the type of base learners in the ensemble. Must be one of: "RULES_AND_LINEAR", "RULES", "LINEAR". Defaults to RULES_AND_LINEAR.
weights_column: Specify a column to use for the observation weights, which are used for bias correction. The specified weights_column must be included in the specified training_frame. Python only: To use a weights column when passing an H2OFrame to x instead of a list of column names, the specified training_frame must contain the specified weights_column.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.
distribution: Specify the distribution (i.e., the loss function). The options are AUTO, bernoulli, multinomial, gaussian, poisson, gamma, laplace, quantile, huber, or tweedie.
If the distribution is bernoulli, the the response column must be 2-class categorical.
If the distribution is quasibinomial, the response column must be numeric and binary.
If the distribution is multinomial, the response column must be categorical.
If the distribution is poisson, the response column must be numeric.
If the distribution is tweedie, the response column must be numeric.
If the distribution is gaussian, the response column must be numeric.
If the distribution is gamma, the response column must be numeric.
If the distribution is fractionalbinomial, the response column must be numeric between 0 and 1.
If the distribution is negativebinomial, the response must be numeric and non-negative.
If the distribution is ordinal, the response must be categorical with at least 3 levels.
If the distribution is AUTO,
and the response is Enum with cardinality = 2, then the family is automatically determined as bernoulli.
and the response is Enum with cardinality > 2, then the family is automatically determined as multinomial.
and the response is numeric (Real or Int), then the family is automatically determined as gaussian.
The output for the RuleFit model includes:
rule importance in tabular form
training and validation metrics of underlying linear model.
FRIEDMAN, J. H., & POPESCU, B. E. (2008). Predictive learning via rule ensembles. The Annals of Applied Statistics, 2(3), 916-954.
Hi , I’ve prepared the content to update docs. Can you please review it from your POV and add it to the User Guide? Thanks!