Model description
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Intended uses & limitations
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Training Procedure
Hyperparameters
The model is trained with below hyperparameters.
Click to expand
Hyperparameter | Value |
---|---|
memory | |
steps | [('transform', FunctionTransformer(func=<function nan_to_num at 0x00000208B48C4A60>)), ('classifier', XGBClassifier(base_score=None, booster=None, callbacks=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, early_stopping_rounds=None, enable_categorical=False, eval_metric=None, feature_types=None, gamma=None, gpu_id=None, grow_policy=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_bin=None, max_cat_threshold=None, max_cat_to_onehot=None, max_delta_step=None, max_depth=None, max_leaves=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=None, num_parallel_tree=None, predictor=None, random_state=None, ...))] |
verbose | False |
transform | FunctionTransformer(func=<function nan_to_num at 0x00000208B48C4A60>) |
classifier | XGBClassifier(base_score=None, booster=None, callbacks=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, early_stopping_rounds=None, enable_categorical=False, eval_metric=None, feature_types=None, gamma=None, gpu_id=None, grow_policy=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_bin=None, max_cat_threshold=None, max_cat_to_onehot=None, max_delta_step=None, max_depth=None, max_leaves=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=None, num_parallel_tree=None, predictor=None, random_state=None, ...) |
transform__accept_sparse | False |
transform__check_inverse | True |
transform__feature_names_out | |
transform__func | <function nan_to_num at 0x00000208B48C4A60> |
transform__inv_kw_args | |
transform__inverse_func | |
transform__kw_args | |
transform__validate | False |
classifier__objective | binary:logistic |
classifier__use_label_encoder | |
classifier__base_score | |
classifier__booster | |
classifier__callbacks | |
classifier__colsample_bylevel | |
classifier__colsample_bynode | |
classifier__colsample_bytree | |
classifier__early_stopping_rounds | |
classifier__enable_categorical | False |
classifier__eval_metric | |
classifier__feature_types | |
classifier__gamma | |
classifier__gpu_id | |
classifier__grow_policy | |
classifier__importance_type | |
classifier__interaction_constraints | |
classifier__learning_rate | |
classifier__max_bin | |
classifier__max_cat_threshold | |
classifier__max_cat_to_onehot | |
classifier__max_delta_step | |
classifier__max_depth | |
classifier__max_leaves | |
classifier__min_child_weight | |
classifier__missing | nan |
classifier__monotone_constraints | |
classifier__n_estimators | 100 |
classifier__n_jobs | |
classifier__num_parallel_tree | |
classifier__predictor | |
classifier__random_state | |
classifier__reg_alpha | |
classifier__reg_lambda | |
classifier__sampling_method | |
classifier__scale_pos_weight | |
classifier__subsample | |
classifier__tree_method | |
classifier__validate_parameters | |
classifier__verbosity |
Model Plot
The model plot is below.
Pipeline(steps=[('transform',FunctionTransformer(func=<function nan_to_num at 0x00000208B48C4A60>)),('classifier',XGBClassifier(base_score=None, booster=None, callbacks=None,colsample_bylevel=None, colsample_bynode=None,colsample_bytree=None,early_stopping_rounds=None,enable_categorical=False, eval_metric=None,feature_types=None, gamma=None, gpu_id=None,grow_policy=None, importance_type=None,interaction_constraints=None, learning_rate=None,max_bin=None, max_cat_threshold=None,max_cat_to_onehot=None, max_delta_step=None,max_depth=None, max_leaves=None,min_child_weight=None, missing=nan,monotone_constraints=None, n_estimators=100,n_jobs=None, num_parallel_tree=None,predictor=None, random_state=None, ...))])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
Pipeline(steps=[('transform',FunctionTransformer(func=<function nan_to_num at 0x00000208B48C4A60>)),('classifier',XGBClassifier(base_score=None, booster=None, callbacks=None,colsample_bylevel=None, colsample_bynode=None,colsample_bytree=None,early_stopping_rounds=None,enable_categorical=False, eval_metric=None,feature_types=None, gamma=None, gpu_id=None,grow_policy=None, importance_type=None,interaction_constraints=None, learning_rate=None,max_bin=None, max_cat_threshold=None,max_cat_to_onehot=None, max_delta_step=None,max_depth=None, max_leaves=None,min_child_weight=None, missing=nan,monotone_constraints=None, n_estimators=100,n_jobs=None, num_parallel_tree=None,predictor=None, random_state=None, ...))])
FunctionTransformer(func=<function nan_to_num at 0x00000208B48C4A60>)
XGBClassifier(base_score=None, booster=None, callbacks=None,colsample_bylevel=None, colsample_bynode=None,colsample_bytree=None, early_stopping_rounds=None,enable_categorical=False, eval_metric=None, feature_types=None,gamma=None, gpu_id=None, grow_policy=None, importance_type=None,interaction_constraints=None, learning_rate=None, max_bin=None,max_cat_threshold=None, max_cat_to_onehot=None,max_delta_step=None, max_depth=None, max_leaves=None,min_child_weight=None, missing=nan, monotone_constraints=None,n_estimators=100, n_jobs=None, num_parallel_tree=None,predictor=None, random_state=None, ...)
Evaluation Results
You can find the details about evaluation process and the evaluation results.
Metric | Value |
---|---|
accuracy | 0.976744 |
precision | 0.972624 |
recall | 0.963504 |
f1-score | 0.967959 |
How to Get Started with the Model
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Model Card Authors
This model card is written by following authors:
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Model Card Contact
You can contact the model card authors through following channels: [More Information Needed]
Citation
Below you can find information related to citation.
BibTeX:
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ModelDescription
The model is a product of academic work by Ümit Işıkdağ,more info on me Ümit Işıkdağ.
GettingStarted
!pip install skops # @only first run
#prepare train/test sets (X_train, X_test, y_train, y_test) as usual
import skops.hub_utils as hub_utils
from sklearn.metrics import accuracy_score
y_pred = hub_utils.get_model_output('uisikdag/simple_clasi_okl25', X_test)
accuracy =accuracy_score(y_test, y_pred)
ModelCardAuthor
Ümit Işıkdağ@2023
ModelLimitations
This model is not ready to be used in production.
CitationBibtex
bibtex @inproceedings{...,year={2020}}
ConfusionMatrix
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