Repository: spark Updated Branches: refs/heads/master d3b4831d0 -> 1dbe9896b
[SPARK-17157][SPARKR][FOLLOW-UP] doc fixes ## What changes were proposed in this pull request? a couple of small late finding fixes for doc ## How was this patch tested? manually wangmiao1981 Author: Felix Cheung <[email protected]> Closes #15650 from felixcheung/logitfix. Project: http://git-wip-us.apache.org/repos/asf/spark/repo Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/1dbe9896 Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/1dbe9896 Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/1dbe9896 Branch: refs/heads/master Commit: 1dbe9896b7f30538a5fad2f5d718d035c7906936 Parents: d3b4831 Author: Felix Cheung <[email protected]> Authored: Wed Oct 26 23:02:54 2016 -0700 Committer: Felix Cheung <[email protected]> Committed: Wed Oct 26 23:02:54 2016 -0700 ---------------------------------------------------------------------- R/pkg/R/mllib.R | 26 +++++++++++--------------- 1 file changed, 11 insertions(+), 15 deletions(-) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/spark/blob/1dbe9896/R/pkg/R/mllib.R ---------------------------------------------------------------------- diff --git a/R/pkg/R/mllib.R b/R/pkg/R/mllib.R index e441db9..629f284 100644 --- a/R/pkg/R/mllib.R +++ b/R/pkg/R/mllib.R @@ -111,8 +111,9 @@ setClass("LogisticRegressionModel", representation(jobj = "jobj")) #' @export #' @seealso \link{spark.glm}, \link{glm}, #' @seealso \link{spark.als}, \link{spark.gaussianMixture}, \link{spark.isoreg}, \link{spark.kmeans}, -#' @seealso \link{spark.lda}, \link{spark.mlp}, \link{spark.naiveBayes}, \link{spark.survreg} -#' @seealso \link{spark.logit}, \link{read.ml} +#' @seealso \link{spark.lda}, \link{spark.logit}, \link{spark.mlp}, \link{spark.naiveBayes}, +#' @seealso \link{spark.survreg} +#' @seealso \link{read.ml} NULL #' Makes predictions from a MLlib model @@ -124,7 +125,7 @@ NULL #' @export #' @seealso \link{spark.glm}, \link{glm}, #' @seealso \link{spark.als}, \link{spark.gaussianMixture}, \link{spark.isoreg}, \link{spark.kmeans}, -#' @seealso \link{spark.mlp}, \link{spark.naiveBayes}, \link{spark.survreg}, \link{spark.logit} +#' @seealso \link{spark.logit}, \link{spark.mlp}, \link{spark.naiveBayes}, \link{spark.survreg} NULL write_internal <- function(object, path, overwrite = FALSE) { @@ -671,14 +672,13 @@ setMethod("predict", signature(object = "KMeansModel"), #' @param tol convergence tolerance of iterations. #' @param fitIntercept whether to fit an intercept term. Default is TRUE. #' @param family the name of family which is a description of the label distribution to be used in the model. -#' Supported options: +#' Supported options: Default is "auto". #' \itemize{ #' \item{"auto": Automatically select the family based on the number of classes: #' If number of classes == 1 || number of classes == 2, set to "binomial". #' Else, set to "multinomial".} #' \item{"binomial": Binary logistic regression with pivoting.} -#' \item{"multinomial": Multinomial logistic (softmax) regression without pivoting. -#' Default is "auto".} +#' \item{"multinomial": Multinomial logistic (softmax) regression without pivoting.} #' } #' @param standardization whether to standardize the training features before fitting the model. The coefficients #' of models will be always returned on the original scale, so it will be transparent for @@ -687,14 +687,10 @@ setMethod("predict", signature(object = "KMeansModel"), #' @param thresholds in binary classification, in range [0, 1]. If the estimated probability of class label 1 #' is > threshold, then predict 1, else 0. A high threshold encourages the model to predict 0 #' more often; a low threshold encourages the model to predict 1 more often. Note: Setting this with -#' threshold p is equivalent to setting thresholds c(1-p, p). When threshold is set, any user-set -#' value for thresholds will be cleared. If both threshold and thresholds are set, then they must be -#' equivalent. In multiclass (or binary) classification to adjust the probability of +#' threshold p is equivalent to setting thresholds c(1-p, p). In multiclass (or binary) classification to adjust the probability of #' predicting each class. Array must have length equal to the number of classes, with values > 0, #' excepting that at most one value may be 0. The class with largest value p/t is predicted, where p -#' is the original probability of that class and t is the class's threshold. Note: When thresholds -#' is set, any user-set value for threshold will be cleared. If both threshold and thresholds are -#' set, then they must be equivalent. Default is 0.5. +#' is the original probability of that class and t is the class's threshold. Default is 0.5. #' @param weightCol The weight column name. #' @param aggregationDepth depth for treeAggregate (>= 2). If the dimensions of features or the number of partitions #' are large, this param could be adjusted to a larger size. Default is 2. @@ -724,7 +720,7 @@ setMethod("predict", signature(object = "KMeansModel"), #' write.ml(blr_model, path) #' #' # can also read back the saved model and predict -#' Note that summary deos not work on loaded model +#' # Note that summary deos not work on loaded model #' savedModel <- read.ml(path) #' blr_predict2 <- collect(select(predict(savedModel, binary_df), "prediction")) #' @@ -738,8 +734,8 @@ setMethod("predict", signature(object = "KMeansModel"), #' data <- as.data.frame(cbind(label, feature1, feature2, feature3, feature4)) #' df <- createDataFrame(data) #' -#' Note that summary of multinomial logistic regression is not implemented yet -#' model <- spark.logit(df, label ~ ., family = "multinomial", thresholds=c(0, 1, 1)) +#' # Note that summary of multinomial logistic regression is not implemented yet +#' model <- spark.logit(df, label ~ ., family = "multinomial", thresholds = c(0, 1, 1)) #' predict1 <- collect(select(predict(model, df), "prediction")) #' } #' @note spark.logit since 2.1.0 --------------------------------------------------------------------- To unsubscribe, e-mail: [email protected] For additional commands, e-mail: [email protected]
