Changing the threshold value of the method selection

[RukhovichIV]

As we know, hist method works much faster than its opponents on CPU. It works especially well with a large data size. At the moment, the threshold for choosing between hist and exact in the heuristic is too high (it is 2^22 or ~4M). We compared the performance and metrics for hist and exact on many workloads and came to the conclusion that it would be optimal to choose 2^18 (~250k) as the threshold. Below are brief tables with the best thresholds for different workloads.

We chose the best threshold based on the training time and two testing metrics on each case. It was grid-searched as the power of 2, starting from 256. We used accuracy + log_loss for classification and rmse + r2 for regression. "Optimal threshold" means the minimum data size at which hist starts performing at least as well as exact

Before the start of the training, all the datasets were randomly shuffled. Next, the first N lines from training datasets were selected for training. Full testing datasets were used for testing. The procedure was repeated for hist and exact.

Classification task:

dataset	train size	optimal train threshold	optimal accuracy threshold	optimal cross entropy threshold
airline-ohe	1M	4096	256	262144
higgs1m	1M	512	256	262144
letters	16k	4096	256	2048
plasticc	7k	2048	256	256
santander	190k	32768	256	8192
airline	92M	256	256	262144
bosch	1.184M	131072	256	131072
epsilon	400k	131072	256	400000
fraud	228k	4096	256	65536
higgs	8.8M	512	256	65536
mlsr	3.02M	16384	16384	8192

Regression task:

dataset	train size	optimal train threshold	optimal rmse threshold	optimal r2 threshold
abalone	~3.3k	256	4096	4096
year	464k	16384	262144	262144
mortgage1q	9.01M	1024	65536	65536

HW:
CPU: Intel(R) Xeon(R) Platinum 8280L CPU @ 2.70GHz
Socket(s): 2
Core(s) per socket: 28
Thread(s) per core: 2
RAM: 24*16G

The full table with all numbers can be found here

This PR is a part of making hist method the default when tree_method==auto (dmlc#7049)