RandomForestRegressor - m-reg - default -#

Fitted on a problem type m-reg (see find_suitable_problem), method predict matches output .

RandomForestRegressor(n_estimators=10, n_jobs=8, random_state=0)

index

0

skl_nop

11

skl_nnodes

1388

skl_ntrees

10

skl_max_depth

14

onx_size

59288

onx_nnodes

1

onx_ninits

0

onx_doc_string

onx_ir_version

8

onx_domain

ai.onnx

onx_model_version

0

onx_producer_name

skl2onnx

onx_producer_version

1.11.1

onx_ai.onnx.ml

1

onx_

15

onx_size_optim

59288

onx_nnodes_optim

1

onx_ninits_optim

0

fit_n_features_

4

fit_estimators_.size

10

fit_estimators_.sum|tree_.node_count

1388

fit_estimators_.sum|tree_.leave_count

699

fit_estimators_.n_features_

4

fit_estimators_.max|tree_.max_depth

14

%0 X X float((0, 4)) TreeEnsembleRegressor TreeEnsembleRegressor (TreeEnsembleRegressor) n_targets=2 nodes_falsenodeids=[46 33 30 ..... nodes_featureids=[2 2 2 ... 0 0... nodes_hitrates=[1. 1. 1. ... 1.... nodes_missing_value_tracks_true=[0 0 0 ...... nodes_modes=[b'BRANCH_LEQ' b'BR... nodes_nodeids=[  0   1   2 ... ... nodes_treeids=[0 0 0 ... 9 9 9] nodes_truenodeids=[1 2 3 ... 0 ... nodes_values=[2.7556252 1.55176... post_transform=b'NONE' target_ids=[0 1 0 ... 1 0 1] target_nodeids=[  5   5   7 ...... target_treeids=[0 0 0 ... 9 9 9... target_weights=[0.013 0.063 0.0... X->TreeEnsembleRegressor variable variable float((0, 1)) TreeEnsembleRegressor->variable