| write.ml {SparkR} | R Documentation |
Save the Bernoulli naive Bayes model to the input path.
Save the AFT survival regression model to the input path.
Save the generalized linear model to the input path.
Save the k-means model to the input path.
write.ml(object, path, ...) ## S4 method for signature 'NaiveBayesModel,character' write.ml(object, path, overwrite = FALSE) ## S4 method for signature 'AFTSurvivalRegressionModel,character' write.ml(object, path, overwrite = FALSE) ## S4 method for signature 'GeneralizedLinearRegressionModel,character' write.ml(object, path, overwrite = FALSE) ## S4 method for signature 'KMeansModel,character' write.ml(object, path, overwrite = FALSE)
object |
A fitted Bernoulli naive Bayes model |
path |
The directory where the model is saved |
overwrite |
Overwrites or not if the output path already exists. Default is FALSE which means throw exception if the output path exists. |
object |
A fitted AFT survival regression model |
path |
The directory where the model is saved |
overwrite |
Overwrites or not if the output path already exists. Default is FALSE which means throw exception if the output path exists. |
object |
A fitted generalized linear model |
path |
The directory where the model is saved |
overwrite |
Overwrites or not if the output path already exists. Default is FALSE which means throw exception if the output path exists. |
object |
A fitted k-means model |
path |
The directory where the model is saved |
overwrite |
Overwrites or not if the output path already exists. Default is FALSE which means throw exception if the output path exists. |
## Not run:
##D df <- createDataFrame(sqlContext, infert)
##D model <- spark.naiveBayes(df, education ~ ., laplace = 0)
##D path <- "path/to/model"
##D write.ml(model, path)
## End(Not run)
## Not run:
##D model <- spark.survreg(trainingData, Surv(futime, fustat) ~ ecog_ps + rx)
##D path <- "path/to/model"
##D write.ml(model, path)
## End(Not run)
## Not run:
##D model <- glm(y ~ x, trainingData)
##D path <- "path/to/model"
##D write.ml(model, path)
## End(Not run)
## Not run:
##D model <- spark.kmeans(trainingData, ~ ., k = 2)
##D path <- "path/to/model"
##D write.ml(model, path)
## End(Not run)