title: Holdout vs. K fold cross validation in libsvm
body: <p>I am doing a classification task using libsvm. I have a 10 fold cross validation where the F1 score is 0.80. However, when I split the training dataset into two (one is for training and the other is for testing, which I call it holdout test set) the F1 score drops to 0.65. The split is in .8 to .2 ratio. </p>
  
  <p>So, my question is that is there any significant difference in doing k-fold cross validation vs. holdout test? Which of these two techniques will produce a model that generalizes well? In both cases, my dataset is scaled. </p>