RF: Add Poisson deviance impurity criterion (#4156)

* Adds the poisson impurity criterion to RF, in parity with scikit learn's RF regressor [[here](https://scikit-learn.org/stable/modules/tree.html#regression-criteria)]
EDIT:
* Also adds C++ level testing for RF Objective function gains of Poisson and Gini.

Authors:
  - Venkat (https://github.com/venkywonka)

Approvers:
  - Rory Mitchell (https://github.com/RAMitchell)
  - Dante Gama Dessavre (https://github.com/dantegd)

URL: #4156

cpp/include/cuml/tree/algo_helper.h

@@ -22,6 +22,7 @@ enum CRITERION {
   ENTROPY,
   MSE,
   MAE,
+  POISSON,
   CRITERION_END,
 };
 

cpp/src/decisiontree/batched-levelalgo/builder.cuh

@@ -422,7 +422,7 @@ struct Builder {
     int nHistBins = large_blocks * nbins * colBlks * nclasses;
     CUDA_CHECK(cudaMemsetAsync(hist, 0, sizeof(BinT) * nHistBins, handle.get_stream()));
     ML::PUSH_RANGE("computeSplitClassificationKernel @builder_base.cuh [batched-levelalgo]");
-    ObjectiveT objective(input.numOutputs, params.min_impurity_decrease, params.min_samples_leaf);
+    ObjectiveT objective(input.numOutputs, params.min_samples_leaf);
     computeSplitKernel<DataT, LabelT, IdxT, TPB_DEFAULT>
       <<<grid, TPB_DEFAULT, smemSize, handle.get_stream()>>>(hist,
                                                              params.n_bins,
@@ -456,7 +456,7 @@ struct Builder {
     rmm::device_uvector<InstanceRange> d_instance_ranges(max_batch_size, handle.get_stream());
     rmm::device_uvector<DataT> d_leaves(max_batch_size * input.numOutputs, handle.get_stream());
 
-    ObjectiveT objective(input.numOutputs, params.min_impurity_decrease, params.min_samples_leaf);
+    ObjectiveT objective(input.numOutputs, params.min_samples_leaf);
     for (std::size_t batch_begin = 0; batch_begin < tree->sparsetree.size();
          batch_begin += max_batch_size) {
       std::size_t batch_end  = min(batch_begin + max_batch_size, tree->sparsetree.size());