如何绘制R中的学习曲线？

Question

我想在我的应用程序中绘制一条学习曲线。

样本曲线图如下所示。

学习曲线是以下方差之间的曲线图，

• X轴：样本数（训练集的大小）。
• Y轴：错误（RSS/J（THETA）/成本函数）

它有助于我们观察模型是否具有高偏压或高方差问题。

R中有没有可以帮助获得这个阴谋的软件包？

Answer 1

您可以使用出色的Caret包进行这样的绘图。关于Customizing the tuning process的部分将会非常有帮助。

另外，你可以看看由Joseph Rickert编写的关于R-Bloggers的博客文章。标题分别为"Why Big Data? Learning Curves"和"Learning from Learning Curves"。

UPDATE
我只是做了这个问题Plot learning curves with caret package and R一个职位。我想我的回答对你更有用。为了方便起见，我在这里再次绘制了与R绘制学习曲线相同的答案。但是，我使用流行的caret包来训练我的模型并获得训练和测试集的RMSE误差。

# set seed for reproducibility 
set.seed(7) 

# randomize mtcars 
mtcars <- mtcars[sample(nrow(mtcars)),] 

# split iris data into training and test sets 
mtcarsIndex <- createDataPartition(mtcars$mpg, p = .625, list = F) 
mtcarsTrain <- mtcars[mtcarsIndex,] 
mtcarsTest <- mtcars[-mtcarsIndex,] 

# create empty data frame 
learnCurve <- data.frame(m = integer(21), 
        trainRMSE = integer(21), 
        cvRMSE = integer(21)) 

# test data response feature 
testY <- mtcarsTest$mpg 

# Run algorithms using 10-fold cross validation with 3 repeats 
trainControl <- trainControl(method="repeatedcv", number=10, repeats=3) 
metric <- "RMSE" 

# loop over training examples 
for (i in 3:21) { 
    learnCurve$m[i] <- i 

    # train learning algorithm with size i 
    fit.lm <- train(mpg~., data=mtcarsTrain[1:i,], method="lm", metric=metric, 
      preProc=c("center", "scale"), trControl=trainControl)   
    learnCurve$trainRMSE[i] <- fit.lm$results$RMSE 

    # use trained parameters to predict on test data 
    prediction <- predict(fit.lm, newdata = mtcarsTest[,-1]) 
    rmse <- postResample(prediction, testY) 
    learnCurve$cvRMSE[i] <- rmse[1] 
} 

pdf("LinearRegressionLearningCurve.pdf", width = 7, height = 7, pointsize=12) 

# plot learning curves of training set size vs. error measure 
# for training set and test set 
plot(log(learnCurve$trainRMSE),type = "o",col = "red", xlab = "Training set size", 
      ylab = "Error (RMSE)", main = "Linear Model Learning Curve") 
lines(log(learnCurve$cvRMSE), type = "o", col = "blue") 
legend('topright', c("Train error", "Test error"), lty = c(1,1), lwd = c(2.5, 2.5), 
     col = c("red", "blue")) 

dev.off() 

输出情节如下图所示：