.. _l-BaggingRegressor-m-reg-default--o17: BaggingRegressor - m-reg - default - ===================================== Fitted on a problem type *m-reg* (see :func:`find_suitable_problem `), method `predict` matches output . :: BaggingRegressor(n_jobs=8, random_state=0) +---------------------------------------+----------+ | index | 0 | +=======================================+==========+ | skl_nop | 11 | +---------------------------------------+----------+ | skl_nnodes | 1388 | +---------------------------------------+----------+ | skl_ntrees | 10 | +---------------------------------------+----------+ | skl_max_depth | 14 | +---------------------------------------+----------+ | onx_size | 64082 | +---------------------------------------+----------+ | onx_nnodes | 22 | +---------------------------------------+----------+ | onx_ninits | 1 | +---------------------------------------+----------+ | onx_doc_string | | +---------------------------------------+----------+ | onx_ir_version | 8 | +---------------------------------------+----------+ | onx_domain | ai.onnx | +---------------------------------------+----------+ | onx_model_version | 0 | +---------------------------------------+----------+ | onx_producer_name | skl2onnx | +---------------------------------------+----------+ | onx_producer_version | 1.13.1 | +---------------------------------------+----------+ | onx_ | 17 | +---------------------------------------+----------+ | onx_ai.onnx.ml | 3 | +---------------------------------------+----------+ | onx_op_Reshape | 10 | +---------------------------------------+----------+ | onx_size_optim | 64082 | +---------------------------------------+----------+ | onx_nnodes_optim | 22 | +---------------------------------------+----------+ | onx_ninits_optim | 1 | +---------------------------------------+----------+ | fit_estimators_.size | 10 | +---------------------------------------+----------+ | fit_estimators_.sum|tree_.leave_count | 699 | +---------------------------------------+----------+ | fit_estimators_.max|tree_.max_depth | 14 | +---------------------------------------+----------+ | fit_estimators_.sum|tree_.node_count | 1388 | +---------------------------------------+----------+ .. gdot:: digraph{ size=7; ranksep=0.25; nodesep=0.05; orientation=portrait; X [shape=box color=red label="X\nfloat((0, 4))" fontsize=10]; variable [shape=box color=green label="variable\nfloat((0, 1))" fontsize=10]; shape_tensor [shape=box label="shape_tensor\nint64((3,))\n[ 1 -1 1]" fontsize=10]; variable_0 [shape=box label="variable_0" fontsize=10]; TreeEnsembleRegressor [shape=box style="filled,rounded" color=orange label="TreeEnsembleRegressor\n(TreeEnsembleRegressor)\nn_targets=2\nnodes_falsenodeids=[ 46 33 30...\nnodes_featureids=[2 2 2 0 2 0 3...\nnodes_hitrates=[1. 1. 1. 1. 1. ...\nnodes_missing_value_tracks_true=[0 0 0 0 0...\nnodes_modes=[b'BRANCH_LEQ' b'BR...\nnodes_nodeids=[ 0 1 2 3 ...\nnodes_treeids=[0 0 0 0 0 0 0 0 ...\nnodes_truenodeids=[ 1 2 3 ...\nnodes_values=[2.7556252 1.5517...\npost_transform=b'NONE'\ntarget_ids=[0 1 0 1 0 1 0 1 0 1...\ntarget_nodeids=[ 5 5 7 7...\ntarget_treeids=[0 0 0 0 0 0 0 0...\ntarget_weights=[0.13 0.63 0.04 ..." fontsize=10]; X -> TreeEnsembleRegressor; TreeEnsembleRegressor -> variable_0; variable_3 [shape=box label="variable_3" fontsize=10]; TreeEnsembleRegressor3 [shape=box style="filled,rounded" color=orange label="TreeEnsembleRegressor\n(TreeEnsembleRegressor3)\nn_targets=2\nnodes_falsenodeids=[ 40 7 4...\nnodes_featureids=[2 0 2 0 2 0 0...\nnodes_hitrates=[1. 1. 1. 1. 1. ...\nnodes_missing_value_tracks_true=[0 0 0 0 0...\nnodes_modes=[b'BRANCH_LEQ' b'BR...\nnodes_nodeids=[ 0 1 2 3 ...\nnodes_treeids=[0 0 0 0 0 0 0 0 ...\nnodes_truenodeids=[ 1 2 3 ...\nnodes_values=[2.4861827 4.4261...\npost_transform=b'NONE'\ntarget_ids=[0 1 0 1 0 1 0 1 0 1...\ntarget_nodeids=[ 3 3 5 5...\ntarget_treeids=[0 0 0 0 0 0 0 0...\ntarget_weights=[0.13 0.63 0.04 ..." fontsize=10]; X -> TreeEnsembleRegressor3; TreeEnsembleRegressor3 -> variable_3; variable_1 [shape=box label="variable_1" fontsize=10]; TreeEnsembleRegressor1 [shape=box style="filled,rounded" color=orange label="TreeEnsembleRegressor\n(TreeEnsembleRegressor1)\nn_targets=2\nnodes_falsenodeids=[ 40 13 6...\nnodes_featureids=[2 3 2 3 0 0 0...\nnodes_hitrates=[1. 1. 1. 1. 1. ...\nnodes_missing_value_tracks_true=[0 0 0 0 0...\nnodes_modes=[b'BRANCH_LEQ' b'BR...\nnodes_nodeids=[ 0 1 2 3 ...\nnodes_treeids=[0 0 0 0 0 0 0 0 ...\nnodes_truenodeids=[ 1 2 3 ...\nnodes_values=[2.4172986 0.1834...\npost_transform=b'NONE'\ntarget_ids=[0 1 0 1 0 1 0 1 0 1...\ntarget_nodeids=[ 4 4 5 5...\ntarget_treeids=[0 0 0 0 0 0 0 0...\ntarget_weights=[0. 0.5 0.04 ..." fontsize=10]; X -> TreeEnsembleRegressor1; TreeEnsembleRegressor1 -> variable_1; variable_2 [shape=box label="variable_2" fontsize=10]; TreeEnsembleRegressor2 [shape=box style="filled,rounded" color=orange label="TreeEnsembleRegressor\n(TreeEnsembleRegressor2)\nn_targets=2\nnodes_falsenodeids=[ 36 11 10...\nnodes_featureids=[2 2 0 3 0 0 0...\nnodes_hitrates=[1. 1. 1. 1. 1. ...\nnodes_missing_value_tracks_true=[0 0 0 0 0...\nnodes_modes=[b'BRANCH_LEQ' b'BR...\nnodes_nodeids=[ 0 1 2 3 ...\nnodes_treeids=[0 0 0 0 0 0 0 0 ...\nnodes_truenodeids=[ 1 2 3 ...\nnodes_values=[2.4079013 1.0568...\npost_transform=b'NONE'\ntarget_ids=[0 1 0 1 0 1 0 1 0 1...\ntarget_nodeids=[ 5 5 6 6...\ntarget_treeids=[0 0 0 0 0 0 0 0...\ntarget_weights=[0.04 0.54 0. ..." fontsize=10]; X -> TreeEnsembleRegressor2; TreeEnsembleRegressor2 -> variable_2; variable_7 [shape=box label="variable_7" fontsize=10]; TreeEnsembleRegressor7 [shape=box style="filled,rounded" color=orange label="TreeEnsembleRegressor\n(TreeEnsembleRegressor7)\nn_targets=2\nnodes_falsenodeids=[ 42 39 12...\nnodes_featureids=[2 3 1 2 1 0 0...\nnodes_hitrates=[1. 1. 1. 1. 1. ...\nnodes_missing_value_tracks_true=[0 0 0 0 0...\nnodes_modes=[b'BRANCH_LEQ' b'BR...\nnodes_nodeids=[ 0 1 2 3 ...\nnodes_treeids=[0 0 0 0 0 0 0 0 ...\nnodes_truenodeids=[ 1 2 3 ...\nnodes_values=[ 2.5489838 0.78...\npost_transform=b'NONE'\ntarget_ids=[0 1 0 1 0 1 0 1 0 1...\ntarget_nodeids=[ 6 6 7 7...\ntarget_treeids=[0 0 0 0 0 0 0 0...\ntarget_weights=[0.41 0.91 0.43 ..." fontsize=10]; X -> TreeEnsembleRegressor7; TreeEnsembleRegressor7 -> variable_7; variable_8 [shape=box label="variable_8" fontsize=10]; TreeEnsembleRegressor8 [shape=box style="filled,rounded" color=orange label="TreeEnsembleRegressor\n(TreeEnsembleRegressor8)\nn_targets=2\nnodes_falsenodeids=[ 50 41 8...\nnodes_featureids=[2 2 2 1 1 0 0...\nnodes_hitrates=[1. 1. 1. 1. 1. ...\nnodes_missing_value_tracks_true=[0 0 0 0 0...\nnodes_modes=[b'BRANCH_LEQ' b'BR...\nnodes_nodeids=[ 0 1 2 3 ...\nnodes_treeids=[0 0 0 0 0 0 0 0 ...\nnodes_truenodeids=[ 1 2 3 ...\nnodes_values=[ 2.5489838 1.72...\npost_transform=b'NONE'\ntarget_ids=[0 1 0 1 0 1 0 1 0 1...\ntarget_nodeids=[ 5 5 6 6...\ntarget_treeids=[0 0 0 0 0 0 0 0...\ntarget_weights=[0.13 0.63 0. ..." fontsize=10]; X -> TreeEnsembleRegressor8; TreeEnsembleRegressor8 -> variable_8; variable_5 [shape=box label="variable_5" fontsize=10]; TreeEnsembleRegressor5 [shape=box style="filled,rounded" color=orange label="TreeEnsembleRegressor\n(TreeEnsembleRegressor5)\nn_targets=2\nnodes_falsenodeids=[ 52 35 30...\nnodes_featureids=[2 2 3 1 0 0 2...\nnodes_hitrates=[1. 1. 1. 1. 1. ...\nnodes_missing_value_tracks_true=[0 0 0 0 0...\nnodes_modes=[b'BRANCH_LEQ' b'BR...\nnodes_nodeids=[ 0 1 2 3 ...\nnodes_treeids=[0 0 0 0 0 0 0 0 ...\nnodes_truenodeids=[ 1 2 3 ...\nnodes_values=[2.7056267 1.5517...\npost_transform=b'NONE'\ntarget_ids=[0 1 0 1 0 1 0 1 0 1...\ntarget_nodeids=[ 4 4 7 7...\ntarget_treeids=[0 0 0 0 0 0 0 0...\ntarget_weights=[0.41 0.91 0.13 ..." fontsize=10]; X -> TreeEnsembleRegressor5; TreeEnsembleRegressor5 -> variable_5; variable_4 [shape=box label="variable_4" fontsize=10]; TreeEnsembleRegressor4 [shape=box style="filled,rounded" color=orange label="TreeEnsembleRegressor\n(TreeEnsembleRegressor4)\nn_targets=2\nnodes_falsenodeids=[ 36 31 6...\nnodes_featureids=[2 2 1 0 0 0 1...\nnodes_hitrates=[1. 1. 1. 1. 1. ...\nnodes_missing_value_tracks_true=[0 0 0 0 0...\nnodes_modes=[b'BRANCH_LEQ' b'BR...\nnodes_nodeids=[ 0 1 2 3 ...\nnodes_treeids=[0 0 0 0 0 0 0 0 ...\nnodes_truenodeids=[ 1 2 3 ...\nnodes_values=[2.5489838 1.7306...\npost_transform=b'NONE'\ntarget_ids=[0 1 0 1 0 1 0 1 0 1...\ntarget_nodeids=[ 4 4 5 5...\ntarget_treeids=[0 0 0 0 0 0 0 0...\ntarget_weights=[0.41 0.91 0.38 ..." fontsize=10]; X -> TreeEnsembleRegressor4; TreeEnsembleRegressor4 -> variable_4; variable_9 [shape=box label="variable_9" fontsize=10]; TreeEnsembleRegressor9 [shape=box style="filled,rounded" color=orange label="TreeEnsembleRegressor\n(TreeEnsembleRegressor9)\nn_targets=2\nnodes_falsenodeids=[ 46 7 4...\nnodes_featureids=[2 2 1 0 2 0 0...\nnodes_hitrates=[1. 1. 1. 1. 1. ...\nnodes_missing_value_tracks_true=[0 0 0 0 0...\nnodes_modes=[b'BRANCH_LEQ' b'BR...\nnodes_nodeids=[ 0 1 2 3 ...\nnodes_treeids=[0 0 0 0 0 0 0 0 ...\nnodes_truenodeids=[ 1 2 3 ...\nnodes_values=[2.6641772 1.0219...\npost_transform=b'NONE'\ntarget_ids=[0 1 0 1 0 1 0 1 0 1...\ntarget_nodeids=[ 3 3 5 5...\ntarget_treeids=[0 0 0 0 0 0 0 0...\ntarget_weights=[0.13 0.63 0. ..." fontsize=10]; X -> TreeEnsembleRegressor9; TreeEnsembleRegressor9 -> variable_9; variable_6 [shape=box label="variable_6" fontsize=10]; TreeEnsembleRegressor6 [shape=box style="filled,rounded" color=orange label="TreeEnsembleRegressor\n(TreeEnsembleRegressor6)\nn_targets=2\nnodes_falsenodeids=[ 38 23 4...\nnodes_featureids=[2 1 1 0 1 0 3...\nnodes_hitrates=[1. 1. 1. 1. 1. ...\nnodes_missing_value_tracks_true=[0 0 0 0 0...\nnodes_modes=[b'BRANCH_LEQ' b'BR...\nnodes_nodeids=[ 0 1 2 3 ...\nnodes_treeids=[0 0 0 0 0 0 0 0 ...\nnodes_truenodeids=[ 1 2 3 ...\nnodes_values=[2.532492 3.5203...\npost_transform=b'NONE'\ntarget_ids=[0 1 0 1 0 1 0 1 0 1...\ntarget_nodeids=[ 3 3 5 5...\ntarget_treeids=[0 0 0 0 0 0 0 0...\ntarget_weights=[0.38 0.88 0.01 ..." fontsize=10]; X -> TreeEnsembleRegressor6; TreeEnsembleRegressor6 -> variable_6; reshaped_proba1 [shape=box label="reshaped_proba1" fontsize=10]; Reshape1 [shape=box style="filled,rounded" color=orange label="Reshape\n(Reshape1)" fontsize=10]; variable_1 -> Reshape1; shape_tensor -> Reshape1; Reshape1 -> reshaped_proba1; reshaped_proba4 [shape=box label="reshaped_proba4" fontsize=10]; Reshape4 [shape=box style="filled,rounded" color=orange label="Reshape\n(Reshape4)" fontsize=10]; variable_4 -> Reshape4; shape_tensor -> Reshape4; Reshape4 -> reshaped_proba4; reshaped_proba5 [shape=box label="reshaped_proba5" fontsize=10]; Reshape5 [shape=box style="filled,rounded" color=orange label="Reshape\n(Reshape5)" fontsize=10]; variable_5 -> Reshape5; shape_tensor -> Reshape5; Reshape5 -> reshaped_proba5; reshaped_proba7 [shape=box label="reshaped_proba7" fontsize=10]; Reshape7 [shape=box style="filled,rounded" color=orange label="Reshape\n(Reshape7)" fontsize=10]; variable_7 -> Reshape7; shape_tensor -> Reshape7; Reshape7 -> reshaped_proba7; reshaped_proba8 [shape=box label="reshaped_proba8" fontsize=10]; Reshape8 [shape=box style="filled,rounded" color=orange label="Reshape\n(Reshape8)" fontsize=10]; variable_8 -> Reshape8; shape_tensor -> Reshape8; Reshape8 -> reshaped_proba8; reshaped_proba9 [shape=box label="reshaped_proba9" fontsize=10]; Reshape9 [shape=box style="filled,rounded" color=orange label="Reshape\n(Reshape9)" fontsize=10]; variable_9 -> Reshape9; shape_tensor -> Reshape9; Reshape9 -> reshaped_proba9; reshaped_proba3 [shape=box label="reshaped_proba3" fontsize=10]; Reshape3 [shape=box style="filled,rounded" color=orange label="Reshape\n(Reshape3)" fontsize=10]; variable_3 -> Reshape3; shape_tensor -> Reshape3; Reshape3 -> reshaped_proba3; reshaped_proba6 [shape=box label="reshaped_proba6" fontsize=10]; Reshape6 [shape=box style="filled,rounded" color=orange label="Reshape\n(Reshape6)" fontsize=10]; variable_6 -> Reshape6; shape_tensor -> Reshape6; Reshape6 -> reshaped_proba6; reshaped_proba2 [shape=box label="reshaped_proba2" fontsize=10]; Reshape2 [shape=box style="filled,rounded" color=orange label="Reshape\n(Reshape2)" fontsize=10]; variable_2 -> Reshape2; shape_tensor -> Reshape2; Reshape2 -> reshaped_proba2; reshaped_proba [shape=box label="reshaped_proba" fontsize=10]; Reshape [shape=box style="filled,rounded" color=orange label="Reshape\n(Reshape)" fontsize=10]; variable_0 -> Reshape; shape_tensor -> Reshape; Reshape -> reshaped_proba; merged_proba [shape=box label="merged_proba" fontsize=10]; Concat [shape=box style="filled,rounded" color=orange label="Concat\n(Concat)\naxis=0" fontsize=10]; reshaped_proba -> Concat; reshaped_proba1 -> Concat; reshaped_proba2 -> Concat; reshaped_proba3 -> Concat; reshaped_proba4 -> Concat; reshaped_proba5 -> Concat; reshaped_proba6 -> Concat; reshaped_proba7 -> Concat; reshaped_proba8 -> Concat; reshaped_proba9 -> Concat; Concat -> merged_proba; ReduceMean [shape=box style="filled,rounded" color=orange label="ReduceMean\n(ReduceMean)\naxes=[0]\nkeepdims=0" fontsize=10]; merged_proba -> ReduceMean; ReduceMean -> variable; }