SEO Expert for Desklib

Solution 3:

Solution 7: N-fold(leave-out out - LOOCV) approximates ErrT well and uses full training
sample to fit a new test point. 10-fold CV estimates Err well and averages over somewhat
different training sets. 10 fold does better job than n-fold in estimating ErrT and Err(expected
error) better.
Solution 8: R CODE:
void Updation(int iter, DMatrix* train) override {
this->Matrix(train);
this->Predict(train, &preds_);
obj_->Gradientfind(preds_, train->info(), iter, &gpair_);
gbm_->Bst(train, &gpair_, obj_.get());
}
void Gradientfind(const std::vector<bst_float> &preds,
const MetaInfo &info,
int iter, std::vector<bst_gpair> *out_gpair) override {
const omp_ulong ndata = static_cast<omp_ulong>(preds.size());
for (omp_ulong i = 0; i < ndata; ++i) {
bst_float p = Loss::PredTransform(preds[i]);
bst_float w = info.GetWeight(i);