---
library_name: sklearn
tags:
- sklearn
- skops
- tabular-classification
model_file: xgb_model_bayse_optimization_00000.bin
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---

# 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           |
|-------------------------|-----------------|
| objective               | binary:logistic |
| use_label_encoder       | True            |
| base_score              | 0.5             |
| booster                 | gbtree          |
| colsample_bylevel       | 1               |
| colsample_bynode        | 1               |
| colsample_bytree        | 1               |
| enable_categorical      | False           |
| gamma                   | 0               |
| gpu_id                  | -1              |
| importance_type         |                 |
| interaction_constraints |                 |
| learning_rate           | 0.300000012     |
| max_delta_step          | 0               |
| max_depth               | 6               |
| min_child_weight        | 1               |
| missing                 | nan             |
| monotone_constraints    | ()              |
| n_estimators            | 100             |
| n_jobs                  | 8               |
| num_parallel_tree       | 1               |
| predictor               | auto            |
| random_state            | 0               |
| reg_alpha               | 0               |
| reg_lambda              | 1               |
| scale_pos_weight        |                 |
| subsample               | 1               |
| tree_method             | auto            |
| validate_parameters     | 1               |
| verbosity               |                 |

</details>

### Model Plot

The model plot is below.

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See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-913bd997-a708-43ce-9527-b1095a5811c6 div.sk-text-repr-fallback {display: none;}</style><div id="sk-913bd997-a708-43ce-9527-b1095a5811c6" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>XGBClassifier(base_score=0.5, booster=&#x27;gbtree&#x27;, colsample_bylevel=1,colsample_bynode=1, colsample_bytree=1, enable_categorical=False,gamma=0, gpu_id=-1, importance_type=None,interaction_constraints=&#x27;&#x27;, learning_rate=0.300000012,max_delta_step=0, max_depth=6, min_child_weight=1, missing=nan,monotone_constraints=&#x27;()&#x27;, n_estimators=100, n_jobs=8,num_parallel_tree=1, predictor=&#x27;auto&#x27;, random_state=0,reg_alpha=0, reg_lambda=1, scale_pos_weight=None, subsample=1,tree_method=&#x27;auto&#x27;, validate_parameters=1, verbosity=None)</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="7575e916-5d25-4b62-9977-129209196a1f" type="checkbox" checked><label for="7575e916-5d25-4b62-9977-129209196a1f" class="sk-toggleable__label sk-toggleable__label-arrow">XGBClassifier</label><div class="sk-toggleable__content"><pre>XGBClassifier(base_score=0.5, booster=&#x27;gbtree&#x27;, colsample_bylevel=1,colsample_bynode=1, colsample_bytree=1, enable_categorical=False,gamma=0, gpu_id=-1, importance_type=None,interaction_constraints=&#x27;&#x27;, learning_rate=0.300000012,max_delta_step=0, max_depth=6, min_child_weight=1, missing=nan,monotone_constraints=&#x27;()&#x27;, n_estimators=100, n_jobs=8,num_parallel_tree=1, predictor=&#x27;auto&#x27;, random_state=0,reg_alpha=0, reg_lambda=1, scale_pos_weight=None, subsample=1,tree_method=&#x27;auto&#x27;, validate_parameters=1, verbosity=None)</pre></div></div></div></div></div>

## Evaluation Results

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



| Metric   | Value   |
|----------|---------|

# How to Get Started with the Model

Use the code below to get started with the model.

```python
import joblib
import json
import pandas as pd
clf = joblib.load(xgb_model_bayse_optimization_00000.bin)
with open("config.json") as f:
    config = json.load(f)
clf.predict(pd.DataFrame.from_dict(config["sklearn"]["example_input"]))
```


# 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:
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# Citation

Below you can find information related to citation.

**BibTeX:**
```
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```