h2o.decision_tree | R Documentation |
Builds a Decision Tree model on an H2OFrame.
h2o.decision_tree(
x,
y,
training_frame,
model_id = NULL,
ignore_const_cols = TRUE,
categorical_encoding = c("AUTO", "Enum", "OneHotInternal", "OneHotExplicit", "Binary",
"Eigen", "LabelEncoder", "SortByResponse", "EnumLimited"),
seed = -1,
max_depth = 20,
min_rows = 10
)
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; auto-generated if not specified. |
ignore_const_cols |
|
categorical_encoding |
Encoding scheme for categorical features Must be one of: "AUTO", "Enum", "OneHotInternal", "OneHotExplicit", "Binary", "Eigen", "LabelEncoder", "SortByResponse", "EnumLimited". Defaults to AUTO. |
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). |
max_depth |
Max depth of tree. Defaults to 20. |
min_rows |
Fewest allowed (weighted) observations in a leaf. Defaults to 10. |
Creates a H2OModel object of the right type.
predict.H2OModel
for prediction
## Not run:
library(h2o)
h2o.init()
# Import the airlines dataset
f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate.csv"
data <- h2o.importFile(f)
# Set predictors and response; set response as a factor
data["CAPSULE"] <- as.factor(data["CAPSULE"])
predictors <- c("AGE","RACE","DPROS","DCAPS","PSA","VOL","GLEASON")
response <- "CAPSULE"
# Train the DT model
h2o_dt <- h2o.decision_tree(x = predictors, y = response, training_frame = data, seed = 1234)
## End(Not run)
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