RandomForestClassifier - m-cl - default - {‘zipmap’: False}#
Fitted on a problem type m-cl
(see find_suitable_problem),
method predict_proba matches output .
Model was converted with additional parameter: <class 'sklearn.ensemble._forest.RandomForestClassifier'>={'zipmap': False}.
RandomForestClassifier(n_estimators=10, n_jobs=8, random_state=0)
index |
0 |
|---|---|
skl_nop |
11 |
skl_nnodes |
224 |
skl_ntrees |
10 |
skl_max_depth |
8 |
onx_size |
11385 |
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 |
15 |
|
onx_size_optim |
11385 |
onx_nnodes_optim |
1 |
onx_ninits_optim |
0 |
fit_classes_.shape |
3 |
3 |
|
4 |
|
fit_estimators_.size |
10 |
fit_estimators_.n_classes_ |
3 |
fit_estimators_.sum|tree_.node_count |
224 |
fit_estimators_.classes_.shape |
3 |
fit_estimators_.sum|tree_.leave_count |
117 |
fit_estimators_.n_features_ |
4 |
fit_estimators_.max|tree_.max_depth |
8 |