[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 <felixcheung_m@hotmail.com> Closes #15650 from felixcheung/logitfix.
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Felix Cheung
Felix Cheung
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@ -111,8 +111,9 @@ setClass("LogisticRegressionModel", representation(jobj = "jobj"))
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#' @export
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#' @seealso \link{spark.glm}, \link{glm},
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#' @seealso \link{spark.als}, \link{spark.gaussianMixture}, \link{spark.isoreg}, \link{spark.kmeans},
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#' @seealso \link{spark.lda}, \link{spark.mlp}, \link{spark.naiveBayes}, \link{spark.survreg}
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#' @seealso \link{spark.logit}, \link{read.ml}
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#' @seealso \link{spark.lda}, \link{spark.logit}, \link{spark.mlp}, \link{spark.naiveBayes},
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#' @seealso \link{spark.survreg}
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#' @seealso \link{read.ml}
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NULL
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#' Makes predictions from a MLlib model
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@ -124,7 +125,7 @@ NULL
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#' @export
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#' @seealso \link{spark.glm}, \link{glm},
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#' @seealso \link{spark.als}, \link{spark.gaussianMixture}, \link{spark.isoreg}, \link{spark.kmeans},
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#' @seealso \link{spark.mlp}, \link{spark.naiveBayes}, \link{spark.survreg}, \link{spark.logit}
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#' @seealso \link{spark.logit}, \link{spark.mlp}, \link{spark.naiveBayes}, \link{spark.survreg}
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NULL
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write_internal <- function(object, path, overwrite = FALSE) {
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@ -671,14 +672,13 @@ setMethod("predict", signature(object = "KMeansModel"),
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#' @param tol convergence tolerance of iterations.
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#' @param fitIntercept whether to fit an intercept term. Default is TRUE.
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#' @param family the name of family which is a description of the label distribution to be used in the model.
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#' Supported options:
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#' Supported options: Default is "auto".
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#' \itemize{
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#' \item{"auto": Automatically select the family based on the number of classes:
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#' If number of classes == 1 || number of classes == 2, set to "binomial".
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#' Else, set to "multinomial".}
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#' \item{"binomial": Binary logistic regression with pivoting.}
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#' \item{"multinomial": Multinomial logistic (softmax) regression without pivoting.
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#' Default is "auto".}
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#' \item{"multinomial": Multinomial logistic (softmax) regression without pivoting.}
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#' }
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#' @param standardization whether to standardize the training features before fitting the model. The coefficients
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#' of models will be always returned on the original scale, so it will be transparent for
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@ -687,14 +687,10 @@ setMethod("predict", signature(object = "KMeansModel"),
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#' @param thresholds in binary classification, in range [0, 1]. If the estimated probability of class label 1
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#' is > threshold, then predict 1, else 0. A high threshold encourages the model to predict 0
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#' more often; a low threshold encourages the model to predict 1 more often. Note: Setting this with
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#' threshold p is equivalent to setting thresholds c(1-p, p). When threshold is set, any user-set
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#' value for thresholds will be cleared. If both threshold and thresholds are set, then they must be
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#' equivalent. In multiclass (or binary) classification to adjust the probability of
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#' threshold p is equivalent to setting thresholds c(1-p, p). In multiclass (or binary) classification to adjust the probability of
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#' predicting each class. Array must have length equal to the number of classes, with values > 0,
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#' excepting that at most one value may be 0. The class with largest value p/t is predicted, where p
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#' is the original probability of that class and t is the class's threshold. Note: When thresholds
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#' is set, any user-set value for threshold will be cleared. If both threshold and thresholds are
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#' set, then they must be equivalent. Default is 0.5.
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#' is the original probability of that class and t is the class's threshold. Default is 0.5.
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#' @param weightCol The weight column name.
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#' @param aggregationDepth depth for treeAggregate (>= 2). If the dimensions of features or the number of partitions
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#' are large, this param could be adjusted to a larger size. Default is 2.
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@ -724,7 +720,7 @@ setMethod("predict", signature(object = "KMeansModel"),
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#' write.ml(blr_model, path)
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#'
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#' # can also read back the saved model and predict
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#' Note that summary deos not work on loaded model
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#' # Note that summary deos not work on loaded model
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#' savedModel <- read.ml(path)
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#' blr_predict2 <- collect(select(predict(savedModel, binary_df), "prediction"))
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#'
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@ -738,8 +734,8 @@ setMethod("predict", signature(object = "KMeansModel"),
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#' data <- as.data.frame(cbind(label, feature1, feature2, feature3, feature4))
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#' df <- createDataFrame(data)
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#'
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#' Note that summary of multinomial logistic regression is not implemented yet
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#' model <- spark.logit(df, label ~ ., family = "multinomial", thresholds=c(0, 1, 1))
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#' # Note that summary of multinomial logistic regression is not implemented yet
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#' model <- spark.logit(df, label ~ ., family = "multinomial", thresholds = c(0, 1, 1))
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#' predict1 <- collect(select(predict(model, df), "prediction"))
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#' }
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#' @note spark.logit since 2.1.0
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