Model description

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Intended uses & limitations

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Training Procedure

Hyperparameters

The model is trained with below hyperparameters.

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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, objective='multi:softprob', predictor=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, objective='multi:softprob', predictor=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	multi:softprob
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,gro...licy=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,objective='multi:softprob', predictor=None, ...))])

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Evaluation Results

You can find the details about evaluation process and the evaluation results.

Metric	Value
accuracy	0.906667
precision	0.902185
recall	0.904874
f1-score	0.902661

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 Yaren Aydın,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_yaren', 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