.. _l-BaggingClassifier-b-cl-default--o17: BaggingClassifier - b-cl - default - ===================================== Fitted on a problem type *b-cl* (see :func:`find_suitable_problem `), method `predict_proba` matches output . :: BaggingClassifier(n_jobs=8, random_state=0) +---------------------------------------+----------+ | index | 0 | +=======================================+==========+ | skl_nop | 11 | +---------------------------------------+----------+ | skl_nnodes | 30 | +---------------------------------------+----------+ | skl_ntrees | 10 | +---------------------------------------+----------+ | skl_max_depth | 1 | +---------------------------------------+----------+ | onx_size | 7080 | +---------------------------------------+----------+ | onx_nnodes | 29 | +---------------------------------------+----------+ | onx_ninits | 3 | +---------------------------------------+----------+ | 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_ai.onnx.ml | 3 | +---------------------------------------+----------+ | onx_ | 17 | +---------------------------------------+----------+ | onx_op_Cast | 3 | +---------------------------------------+----------+ | onx_op_ZipMap | 1 | +---------------------------------------+----------+ | onx_op_Reshape | 11 | +---------------------------------------+----------+ | onx_size_optim | 5880 | +---------------------------------------+----------+ | onx_nnodes_optim | 25 | +---------------------------------------+----------+ | onx_ninits_optim | 3 | +---------------------------------------+----------+ | fit_classes_.shape | 2 | +---------------------------------------+----------+ | fit_n_classes_ | 2 | +---------------------------------------+----------+ | fit_estimators_.size | 10 | +---------------------------------------+----------+ | fit_estimators_.n_classes_ | 2 | +---------------------------------------+----------+ | fit_estimators_.classes_.shape | 2 | +---------------------------------------+----------+ | fit_estimators_.sum|tree_.leave_count | 20 | +---------------------------------------+----------+ | fit_estimators_.max|tree_.max_depth | 1 | +---------------------------------------+----------+ | fit_estimators_.sum|tree_.node_count | 30 | +---------------------------------------+----------+ .. gdot:: digraph{ size=7; ranksep=0.25; nodesep=0.05; orientation=portrait; X [shape=box color=red label="X\nfloat((0, 4))" fontsize=10]; output_label [shape=box color=green label="output_label\nint64((0,))" fontsize=10]; output_probability [shape=box color=green label="output_probability\n[{int64, {'kind': 'tensor', 'elem': 'float', 'shape': }}]" fontsize=10]; classes [shape=box label="classes\nint32((2,))\n[0 1]" fontsize=10]; shape_tensor [shape=box label="shape_tensor\nint64((3,))\n[ 1 -1 2]" fontsize=10]; shape_tensor10 [shape=box label="shape_tensor10\nint64((1,))\n[-1]" fontsize=10]; label_0 [shape=box label="label_0" fontsize=10]; proba_0 [shape=box label="proba_0" fontsize=10]; TreeEnsembleClassifier [shape=box style="filled,rounded" color=orange label="TreeEnsembleClassifier\n(TreeEnsembleClassifier)\nclass_ids=[0 0]\nclass_nodeids=[1 2]\nclass_treeids=[0 0]\nclass_weights=[0. 1.]\nclasslabels_int64s=[0 1]\nnodes_falsenodeids=[2 0 0]\nnodes_featureids=[2 0 0]\nnodes_hitrates=[1. 1. 1.]\nnodes_missing_value_tracks_true=[0 0 0]\nnodes_modes=[b'BRANCH_LEQ' b'LE...\nnodes_nodeids=[0 1 2]\nnodes_treeids=[0 0 0]\nnodes_truenodeids=[1 0 0]\nnodes_values=[2.7556252 0. ...\npost_transform=b'NONE'" fontsize=10]; X -> TreeEnsembleClassifier; TreeEnsembleClassifier -> label_0; TreeEnsembleClassifier -> proba_0; label_1 [shape=box label="label_1" fontsize=10]; proba_1 [shape=box label="proba_1" fontsize=10]; TreeEnsembleClassifier1 [shape=box style="filled,rounded" color=orange label="TreeEnsembleClassifier\n(TreeEnsembleClassifier1)\nclass_ids=[0 0]\nclass_nodeids=[1 2]\nclass_treeids=[0 0]\nclass_weights=[0. 1.]\nclasslabels_int64s=[0 1]\nnodes_falsenodeids=[2 0 0]\nnodes_featureids=[2 0 0]\nnodes_hitrates=[1. 1. 1.]\nnodes_missing_value_tracks_true=[0 0 0]\nnodes_modes=[b'BRANCH_LEQ' b'LE...\nnodes_nodeids=[0 1 2]\nnodes_treeids=[0 0 0]\nnodes_truenodeids=[1 0 0]\nnodes_values=[2.4172986 0. ...\npost_transform=b'NONE'" fontsize=10]; X -> TreeEnsembleClassifier1; TreeEnsembleClassifier1 -> label_1; TreeEnsembleClassifier1 -> proba_1; label_7 [shape=box label="label_7" fontsize=10]; proba_7 [shape=box label="proba_7" fontsize=10]; TreeEnsembleClassifier7 [shape=box style="filled,rounded" color=orange label="TreeEnsembleClassifier\n(TreeEnsembleClassifier7)\nclass_ids=[0 0]\nclass_nodeids=[1 2]\nclass_treeids=[0 0]\nclass_weights=[0. 1.]\nclasslabels_int64s=[0 1]\nnodes_falsenodeids=[2 0 0]\nnodes_featureids=[2 0 0]\nnodes_hitrates=[1. 1. 1.]\nnodes_missing_value_tracks_true=[0 0 0]\nnodes_modes=[b'BRANCH_LEQ' b'LE...\nnodes_nodeids=[0 1 2]\nnodes_treeids=[0 0 0]\nnodes_truenodeids=[1 0 0]\nnodes_values=[2.5489838 0. ...\npost_transform=b'NONE'" fontsize=10]; X -> TreeEnsembleClassifier7; TreeEnsembleClassifier7 -> label_7; TreeEnsembleClassifier7 -> proba_7; label_6 [shape=box label="label_6" fontsize=10]; proba_6 [shape=box label="proba_6" fontsize=10]; TreeEnsembleClassifier6 [shape=box style="filled,rounded" color=orange label="TreeEnsembleClassifier\n(TreeEnsembleClassifier6)\nclass_ids=[0 0]\nclass_nodeids=[1 2]\nclass_treeids=[0 0]\nclass_weights=[0. 1.]\nclasslabels_int64s=[0 1]\nnodes_falsenodeids=[2 0 0]\nnodes_featureids=[2 0 0]\nnodes_hitrates=[1. 1. 1.]\nnodes_missing_value_tracks_true=[0 0 0]\nnodes_modes=[b'BRANCH_LEQ' b'LE...\nnodes_nodeids=[0 1 2]\nnodes_treeids=[0 0 0]\nnodes_truenodeids=[1 0 0]\nnodes_values=[2.532492 0. ...\npost_transform=b'NONE'" fontsize=10]; X -> TreeEnsembleClassifier6; TreeEnsembleClassifier6 -> label_6; TreeEnsembleClassifier6 -> proba_6; label_8 [shape=box label="label_8" fontsize=10]; proba_8 [shape=box label="proba_8" fontsize=10]; TreeEnsembleClassifier8 [shape=box style="filled,rounded" color=orange label="TreeEnsembleClassifier\n(TreeEnsembleClassifier8)\nclass_ids=[0 0]\nclass_nodeids=[1 2]\nclass_treeids=[0 0]\nclass_weights=[0. 1.]\nclasslabels_int64s=[0 1]\nnodes_falsenodeids=[2 0 0]\nnodes_featureids=[2 0 0]\nnodes_hitrates=[1. 1. 1.]\nnodes_missing_value_tracks_true=[0 0 0]\nnodes_modes=[b'BRANCH_LEQ' b'LE...\nnodes_nodeids=[0 1 2]\nnodes_treeids=[0 0 0]\nnodes_truenodeids=[1 0 0]\nnodes_values=[2.5489838 0. ...\npost_transform=b'NONE'" fontsize=10]; X -> TreeEnsembleClassifier8; TreeEnsembleClassifier8 -> label_8; TreeEnsembleClassifier8 -> proba_8; label_9 [shape=box label="label_9" fontsize=10]; proba_9 [shape=box label="proba_9" fontsize=10]; TreeEnsembleClassifier9 [shape=box style="filled,rounded" color=orange label="TreeEnsembleClassifier\n(TreeEnsembleClassifier9)\nclass_ids=[0 0]\nclass_nodeids=[1 2]\nclass_treeids=[0 0]\nclass_weights=[0. 1.]\nclasslabels_int64s=[0 1]\nnodes_falsenodeids=[2 0 0]\nnodes_featureids=[2 0 0]\nnodes_hitrates=[1. 1. 1.]\nnodes_missing_value_tracks_true=[0 0 0]\nnodes_modes=[b'BRANCH_LEQ' b'LE...\nnodes_nodeids=[0 1 2]\nnodes_treeids=[0 0 0]\nnodes_truenodeids=[1 0 0]\nnodes_values=[2.6641772 0. ...\npost_transform=b'NONE'" fontsize=10]; X -> TreeEnsembleClassifier9; TreeEnsembleClassifier9 -> label_9; TreeEnsembleClassifier9 -> proba_9; label_3 [shape=box label="label_3" fontsize=10]; proba_3 [shape=box label="proba_3" fontsize=10]; TreeEnsembleClassifier3 [shape=box style="filled,rounded" color=orange label="TreeEnsembleClassifier\n(TreeEnsembleClassifier3)\nclass_ids=[0 0]\nclass_nodeids=[1 2]\nclass_treeids=[0 0]\nclass_weights=[0. 1.]\nclasslabels_int64s=[0 1]\nnodes_falsenodeids=[2 0 0]\nnodes_featureids=[2 0 0]\nnodes_hitrates=[1. 1. 1.]\nnodes_missing_value_tracks_true=[0 0 0]\nnodes_modes=[b'BRANCH_LEQ' b'LE...\nnodes_nodeids=[0 1 2]\nnodes_treeids=[0 0 0]\nnodes_truenodeids=[1 0 0]\nnodes_values=[2.4861827 0. ...\npost_transform=b'NONE'" fontsize=10]; X -> TreeEnsembleClassifier3; TreeEnsembleClassifier3 -> label_3; TreeEnsembleClassifier3 -> proba_3; label_2 [shape=box label="label_2" fontsize=10]; proba_2 [shape=box label="proba_2" fontsize=10]; TreeEnsembleClassifier2 [shape=box style="filled,rounded" color=orange label="TreeEnsembleClassifier\n(TreeEnsembleClassifier2)\nclass_ids=[0 0]\nclass_nodeids=[1 2]\nclass_treeids=[0 0]\nclass_weights=[0. 1.]\nclasslabels_int64s=[0 1]\nnodes_falsenodeids=[2 0 0]\nnodes_featureids=[2 0 0]\nnodes_hitrates=[1. 1. 1.]\nnodes_missing_value_tracks_true=[0 0 0]\nnodes_modes=[b'BRANCH_LEQ' b'LE...\nnodes_nodeids=[0 1 2]\nnodes_treeids=[0 0 0]\nnodes_truenodeids=[1 0 0]\nnodes_values=[2.4079013 0. ...\npost_transform=b'NONE'" fontsize=10]; X -> TreeEnsembleClassifier2; TreeEnsembleClassifier2 -> label_2; TreeEnsembleClassifier2 -> proba_2; label_4 [shape=box label="label_4" fontsize=10]; proba_4 [shape=box label="proba_4" fontsize=10]; TreeEnsembleClassifier4 [shape=box style="filled,rounded" color=orange label="TreeEnsembleClassifier\n(TreeEnsembleClassifier4)\nclass_ids=[0 0]\nclass_nodeids=[1 2]\nclass_treeids=[0 0]\nclass_weights=[0. 1.]\nclasslabels_int64s=[0 1]\nnodes_falsenodeids=[2 0 0]\nnodes_featureids=[2 0 0]\nnodes_hitrates=[1. 1. 1.]\nnodes_missing_value_tracks_true=[0 0 0]\nnodes_modes=[b'BRANCH_LEQ' b'LE...\nnodes_nodeids=[0 1 2]\nnodes_treeids=[0 0 0]\nnodes_truenodeids=[1 0 0]\nnodes_values=[2.5489838 0. ...\npost_transform=b'NONE'" fontsize=10]; X -> TreeEnsembleClassifier4; TreeEnsembleClassifier4 -> label_4; TreeEnsembleClassifier4 -> proba_4; label_5 [shape=box label="label_5" fontsize=10]; proba_5 [shape=box label="proba_5" fontsize=10]; TreeEnsembleClassifier5 [shape=box style="filled,rounded" color=orange label="TreeEnsembleClassifier\n(TreeEnsembleClassifier5)\nclass_ids=[0 0]\nclass_nodeids=[1 2]\nclass_treeids=[0 0]\nclass_weights=[0. 1.]\nclasslabels_int64s=[0 1]\nnodes_falsenodeids=[2 0 0]\nnodes_featureids=[2 0 0]\nnodes_hitrates=[1. 1. 1.]\nnodes_missing_value_tracks_true=[0 0 0]\nnodes_modes=[b'BRANCH_LEQ' b'LE...\nnodes_nodeids=[0 1 2]\nnodes_treeids=[0 0 0]\nnodes_truenodeids=[1 0 0]\nnodes_values=[2.7056267 0. ...\npost_transform=b'NONE'" fontsize=10]; X -> TreeEnsembleClassifier5; TreeEnsembleClassifier5 -> label_5; TreeEnsembleClassifier5 -> proba_5; reshaped_proba5 [shape=box label="reshaped_proba5" fontsize=10]; Reshape5 [shape=box style="filled,rounded" color=orange label="Reshape\n(Reshape5)" fontsize=10]; proba_5 -> Reshape5; shape_tensor -> Reshape5; Reshape5 -> reshaped_proba5; reshaped_proba1 [shape=box label="reshaped_proba1" fontsize=10]; Reshape1 [shape=box style="filled,rounded" color=orange label="Reshape\n(Reshape1)" fontsize=10]; proba_1 -> Reshape1; shape_tensor -> Reshape1; Reshape1 -> reshaped_proba1; reshaped_proba2 [shape=box label="reshaped_proba2" fontsize=10]; Reshape2 [shape=box style="filled,rounded" color=orange label="Reshape\n(Reshape2)" fontsize=10]; proba_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]; proba_0 -> Reshape; shape_tensor -> Reshape; Reshape -> reshaped_proba; reshaped_proba9 [shape=box label="reshaped_proba9" fontsize=10]; Reshape9 [shape=box style="filled,rounded" color=orange label="Reshape\n(Reshape9)" fontsize=10]; proba_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]; proba_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]; proba_6 -> Reshape6; shape_tensor -> Reshape6; Reshape6 -> reshaped_proba6; reshaped_proba4 [shape=box label="reshaped_proba4" fontsize=10]; Reshape4 [shape=box style="filled,rounded" color=orange label="Reshape\n(Reshape4)" fontsize=10]; proba_4 -> Reshape4; shape_tensor -> Reshape4; Reshape4 -> reshaped_proba4; reshaped_proba7 [shape=box label="reshaped_proba7" fontsize=10]; Reshape7 [shape=box style="filled,rounded" color=orange label="Reshape\n(Reshape7)" fontsize=10]; proba_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]; proba_8 -> Reshape8; shape_tensor -> Reshape8; Reshape8 -> reshaped_proba8; 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; probabilities [shape=box label="probabilities" fontsize=10]; ReduceMean [shape=box style="filled,rounded" color=orange label="ReduceMean\n(ReduceMean)\naxes=[0]\nkeepdims=0" fontsize=10]; merged_proba -> ReduceMean; ReduceMean -> probabilities; argmax_output [shape=box label="argmax_output" fontsize=10]; ArgMax [shape=box style="filled,rounded" color=orange label="ArgMax\n(ArgMax)\naxis=1" fontsize=10]; probabilities -> ArgMax; ArgMax -> argmax_output; ZipMap [shape=box style="filled,rounded" color=orange label="ZipMap\n(ZipMap)\nclasslabels_int64s=[0 1]" fontsize=10]; probabilities -> ZipMap; ZipMap -> output_probability; array_feature_extractor_result [shape=box label="array_feature_extractor_result" fontsize=10]; ArrayFeatureExtractor [shape=box style="filled,rounded" color=orange label="ArrayFeatureExtractor\n(ArrayFeatureExtractor)" fontsize=10]; classes -> ArrayFeatureExtractor; argmax_output -> ArrayFeatureExtractor; ArrayFeatureExtractor -> array_feature_extractor_result; cast_result [shape=box label="cast_result" fontsize=10]; Cast [shape=box style="filled,rounded" color=orange label="Cast\n(Cast)\nto=7" fontsize=10]; array_feature_extractor_result -> Cast; Cast -> cast_result; reshaped_result [shape=box label="reshaped_result" fontsize=10]; Reshape10 [shape=box style="filled,rounded" color=orange label="Reshape\n(Reshape10)" fontsize=10]; cast_result -> Reshape10; shape_tensor10 -> Reshape10; Reshape10 -> reshaped_result; label [shape=box label="label" fontsize=10]; Cast1 [shape=box style="filled,rounded" color=orange label="Cast\n(Cast1)\nto=7" fontsize=10]; reshaped_result -> Cast1; Cast1 -> label; Cast2 [shape=box style="filled,rounded" color=orange label="Cast\n(Cast2)\nto=7" fontsize=10]; label -> Cast2; Cast2 -> output_label; }