
# Model description

[More Information Needed]

## Intended uses & limitations

[More Information Needed]

## Training Procedure

### Hyperparameters

The model is trained with below hyperparameters.

<details>
<summary> Click to expand </summary>

| Hyperparameter                      | Value                                                                 |
|-------------------------------------|-----------------------------------------------------------------------|
| memory                              |                                                                       |
| steps                               | [('transform', FunctionTransformer(func=<function nan_to_num at 0x00000208B48C4A60>)), ('classifier', XGBClassifier(base_score=None, booster=None, callbacks=None,<br />              colsample_bylevel=None, colsample_bynode=None,<br />              colsample_bytree=None, early_stopping_rounds=None,<br />              enable_categorical=False, eval_metric=None, feature_types=None,<br />              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,<br />              interaction_constraints=None, learning_rate=None, max_bin=None,<br />              max_cat_threshold=None, max_cat_to_onehot=None,<br />              max_delta_step=None, max_depth=None, max_leaves=None,<br />              min_child_weight=None, missing=nan, monotone_constraints=None,<br />              n_estimators=100, n_jobs=None, num_parallel_tree=None,<br />              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,<br />              colsample_bylevel=None, colsample_bynode=None,<br />              colsample_bytree=None, early_stopping_rounds=None,<br />              enable_categorical=False, eval_metric=None, feature_types=None,<br />              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,<br />              interaction_constraints=None, learning_rate=None, max_bin=None,<br />              max_cat_threshold=None, max_cat_to_onehot=None,<br />              max_delta_step=None, max_depth=None, max_leaves=None,<br />              min_child_weight=None, missing=nan, monotone_constraints=None,<br />              n_estimators=100, n_jobs=None, num_parallel_tree=None,<br />              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               |                                                                       |

</details>

### Model Plot

The model plot is below.

<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-1" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;transform&#x27;,FunctionTransformer(func=&lt;function nan_to_num at 0x00000208B48C4A60&gt;)),(&#x27;classifier&#x27;,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, ...))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-1" type="checkbox" ><label for="sk-estimator-id-1" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[(&#x27;transform&#x27;,FunctionTransformer(func=&lt;function nan_to_num at 0x00000208B48C4A60&gt;)),(&#x27;classifier&#x27;,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, ...))])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-2" type="checkbox" ><label for="sk-estimator-id-2" class="sk-toggleable__label sk-toggleable__label-arrow">FunctionTransformer</label><div class="sk-toggleable__content"><pre>FunctionTransformer(func=&lt;function nan_to_num at 0x00000208B48C4A60&gt;)</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-3" type="checkbox" ><label for="sk-estimator-id-3" class="sk-toggleable__label sk-toggleable__label-arrow">XGBClassifier</label><div class="sk-toggleable__content"><pre>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, ...)</pre></div></div></div></div></div></div></div>

## 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

[More Information Needed]

# Model Card Authors

This model card is written by following authors:

[More Information Needed]

# 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:**
```
[More Information Needed]
```

# ModelDescription

The model is a product of academic work by Ümit Işıkdağ,more info on me [Ümit Işıkdağ](https://isikdag.com).

# GettingStarted

<i>!pip install skops # @only first run<br>
                      #prepare train/test sets (X_train, X_test, y_train, y_test) as usual<br>
                      import skops.hub_utils as hub_utils <br> 
                      from sklearn.metrics import accuracy_score<br>
                      y_pred = hub_utils.get_model_output('uisikdag/simple_clasi_okl25', X_test)<br>
                      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

![ConfusionMatrix](confusion_matrix.png)

{"library_name": "sklearn", "tags": ["sklearn", "skops", "tabular-classification"], "model_file": "umit_class_okl25.pkl", "widget": {"structuredData": {"CO2": [708, 490, 481], "Hum": [31.3, 31.8, 44.9], "Temp": [23.6, 23.1, 21.4]}}}
sklearn skops tabular-classification region:us
TAGS
#sklearn #skops #tabular-classification #region-us 

Model description
=================


Intended uses & limitations
---------------------------


Training Procedure
------------------


### Hyperparameters


The model is trained with below hyperparameters.



 Click to expand 


### Model Plot


The model plot is below.


#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 URL-toggleable {background-color: white;}#sk-container-id-1 URL-toggleable\_\_label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 URL-toggleable\_\_label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 URL-toggleable\_\_label-arrow:hover:before {color: black;}#sk-container-id-1 URL-estimator:hover URL-toggleable\_\_label-arrow:before {color: black;}#sk-container-id-1 URL-toggleable\_\_content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 URL-toggleable\_\_content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 URL-toggleable\_\_control:checked~URL-toggleable\_\_content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 URL-toggleable\_\_control:checked~URL-toggleable\_\_label-arrow:before {content: "▾";}#sk-container-id-1 URL-estimator URL-toggleable\_\_control:checked~URL-toggleable\_\_label {background-color: #d4ebff;}#sk-container-id-1 URL-label URL-toggleable\_\_control:checked~URL-toggleable\_\_label {background-color: #d4ebff;}#sk-container-id-1 URL-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 URL-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 URL-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 URL-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 URL-label:hover URL-toggleable\_\_label {background-color: #d4ebff;}#sk-container-id-1 URL-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 URL-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 URL-item {position: relative;z-index: 1;}#sk-container-id-1 URL-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 URL-item::before, #sk-container-id-1 URL-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 URL-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 URL-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 URL-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 URL-parallel-item:only-child::after {width: 0;}#sk-container-id-1 URL-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 URL-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 URL-label-container {text-align: center;}#sk-container-id-1 URL-container {/\* jupyter's 'URL' sets '[hidden] { display: none; }' but URL set '[hidden] { display: none !important; }' so we also need the '!important' here to be able to override the default hidden behavior on the sphinx rendered URL. See: URL \*/display: inline-block !important;position: relative;}#sk-container-id-1 URL-text-repr-fallback {display: none;}
```
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 URL.**Pipeline
```
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
```
FunctionTransformer(func=<function nan_to_num at 0x00000208B48C4A60>)
```
XGBClassifier
```
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.



How to Get Started with the Model
=================================


Model Card Authors
==================


This model card is written by following authors:


Model Card Contact
==================


You can contact the model card authors through following channels:


Below you can find information related to citation.


BibTeX:


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.


Bibtex


bibtex
@inproceedings{...,year={2020}}


ConfusionMatrix
===============


!ConfusionMatrix



### Hyperparameters


The model is trained with below hyperparameters.



 Click to expand ### Model Plot


The model plot is below. # @only first run
TAGS
#sklearn #skops #tabular-classification #region-us 
 ### Hyperparameters


The model is trained with below hyperparameters.



 Click to expand ### Model Plot


The model plot is below. # @only first run
TAGS
#sklearn #skops #tabular-classification #region-us 
### Hyperparameters


The model is trained with below hyperparameters.



 Click to expand### Model Plot


The model plot is below.# @only first run