请问哪位用过Rsofia package？以下是在stackoverflow 中看到的资料
"A stochastic gradient descent approach to SVM could help, as it scales well and avoids the n^2 problem. An implementation available in R is RSofia, which was created by a team at Google and is discussed in Large Scale Learning to Rank. In the paper, they show that compared to a traditional SVM, the SGD approach significantly decreases the training time (this is due to 1, the pairwise learning method and 2, only a subset of the observations end up being used to train the model).
Note that RSofia is a little more bare bones than some of the other SVM packages available in R; for example, you need to do your own centering and scaling of features."
我help了Rsofia package中的sofia和predict函数，有如下资料：
data(irismod)
i.TRAIN <- sample(1:nrow(irismod), 100)
model.logreg <- sofia(Is.Virginica ~ ., data=irismod[i.TRAIN,], learner_type="logreg-pegasos")
p <- predict(model.logreg, newdata=irismod[-1*i.TRAIN,], prediction_type = "logistic")
table(predicted=p>0.5, actual=irismod[-1*i.TRAIN,]$Is.Virginica)
model.pegasos <- sofia(Is.Virginica ~ ., data=irismod[i.TRAIN,], learner_type="pegasos")
d <- predict(model.pegasos, newdata=irismod[-1*i.TRAIN,], prediction_type = "linear")
table(predicted=d>0, actual=irismod[-1*i.TRAIN,]$Is.Virginica)
其中sofia函数中的prediction_type的选项有 One of "pegasos" (default), "sgd-svm", "passive-aggressive", "margin-perceptron", "romma", "logreg-pegasos"，请问，这些选项分别是什么意思呢？
其中predict函数中prediction_type 有哪些选项呢，其中的 "linear"是什么意思呢？
不太明白，请教，谢谢！
如果有相关资料提供更好，非常感谢！
"A stochastic gradient descent approach to SVM could help, as it scales well and avoids the n^2 problem. An implementation available in R is RSofia, which was created by a team at Google and is discussed in Large Scale Learning to Rank. In the paper, they show that compared to a traditional SVM, the SGD approach significantly decreases the training time (this is due to 1, the pairwise learning method and 2, only a subset of the observations end up being used to train the model).
Note that RSofia is a little more bare bones than some of the other SVM packages available in R; for example, you need to do your own centering and scaling of features."
我help了Rsofia package中的sofia和predict函数，有如下资料：
data(irismod)
i.TRAIN <- sample(1:nrow(irismod), 100)
model.logreg <- sofia(Is.Virginica ~ ., data=irismod[i.TRAIN,], learner_type="logreg-pegasos")
p <- predict(model.logreg, newdata=irismod[-1*i.TRAIN,], prediction_type = "logistic")
table(predicted=p>0.5, actual=irismod[-1*i.TRAIN,]$Is.Virginica)
model.pegasos <- sofia(Is.Virginica ~ ., data=irismod[i.TRAIN,], learner_type="pegasos")
d <- predict(model.pegasos, newdata=irismod[-1*i.TRAIN,], prediction_type = "linear")
table(predicted=d>0, actual=irismod[-1*i.TRAIN,]$Is.Virginica)
其中sofia函数中的prediction_type的选项有 One of "pegasos" (default), "sgd-svm", "passive-aggressive", "margin-perceptron", "romma", "logreg-pegasos"，请问，这些选项分别是什么意思呢？
其中predict函数中prediction_type 有哪些选项呢，其中的 "linear"是什么意思呢？
不太明白，请教，谢谢！
如果有相关资料提供更好，非常感谢！