---
library_name: sklearn
license: mit
tags:
- sklearn
- skops
- tabular-classification
model_format: skops
model_file: febskxmodel_hug_1.skops
widget:
- structuredData:
    backlog_minutes:
    - 246897
    - 265856
    - 622046
    backlog_num_jobs:
    - 211
    - 298
    - 369
    max_minutes:
    - 360
    - 30
    - 2160
    nnodes:
    - 1
    - 1
    - 1
    running_minutes:
    - 1934324
    - 1934324
    - 1934214
    running_num_jobs:
    - 6830
    - 6830
    - 6829
---

# Model description

[More Information Needed]

## Intended uses & limitations

[More Information Needed]

## Training Procedure

[More Information Needed]

### Hyperparameters

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

| Hyperparameter             | Value                                                                                              |
|----------------------------|----------------------------------------------------------------------------------------------------|
| memory                     |                                                                                                    |
| steps                      | [('scale', StandardScaler()), ('hgbc', HistGradientBoostingClassifier(max_depth=9, max_iter=600))] |
| verbose                    | False                                                                                              |
| scale                      | StandardScaler()                                                                                   |
| hgbc                       | HistGradientBoostingClassifier(max_depth=9, max_iter=600)                                          |
| scale__copy                | True                                                                                               |
| scale__with_mean           | True                                                                                               |
| scale__with_std            | True                                                                                               |
| hgbc__categorical_features |                                                                                                    |
| hgbc__class_weight         |                                                                                                    |
| hgbc__early_stopping       | auto                                                                                               |
| hgbc__interaction_cst      |                                                                                                    |
| hgbc__l2_regularization    | 0.0                                                                                                |
| hgbc__learning_rate        | 0.1                                                                                                |
| hgbc__loss                 | log_loss                                                                                           |
| hgbc__max_bins             | 255                                                                                                |
| hgbc__max_depth            | 9                                                                                                  |
| hgbc__max_iter             | 600                                                                                                |
| hgbc__max_leaf_nodes       | 31                                                                                                 |
| hgbc__min_samples_leaf     | 20                                                                                                 |
| hgbc__monotonic_cst        |                                                                                                    |
| hgbc__n_iter_no_change     | 10                                                                                                 |
| hgbc__random_state         |                                                                                                    |
| hgbc__scoring              | loss                                                                                               |
| hgbc__tol                  | 1e-07                                                                                              |
| hgbc__validation_fraction  | 0.1                                                                                                |
| hgbc__verbose              | 0                                                                                                  |
| hgbc__warm_start           | False                                                                                              |

</details>

### Model Plot

<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;scale&#x27;, StandardScaler()),(&#x27;hgbc&#x27;,HistGradientBoostingClassifier(max_depth=9, max_iter=600))])</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;scale&#x27;, StandardScaler()),(&#x27;hgbc&#x27;,HistGradientBoostingClassifier(max_depth=9, max_iter=600))])</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">StandardScaler</label><div class="sk-toggleable__content"><pre>StandardScaler()</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">HistGradientBoostingClassifier</label><div class="sk-toggleable__content"><pre>HistGradientBoostingClassifier(max_depth=9, max_iter=600)</pre></div></div></div></div></div></div></div>

## Evaluation Results

| Metric                | Value              |
|-----------------------|--------------------|
| accuracy              | 0.9079252003561887 |
| classification report | precision    recall  f1-score   support<br /><br />           0       0.94      0.98      0.96      3580<br />           1       0.76      0.55      0.64       415<br />           2       0.63      0.51      0.56       208<br />           3       0.68      0.47      0.55       160<br />           4       0.91      0.94      0.93      1252<br /><br />    accuracy                           0.91      5615<br />   macro avg       0.78      0.69      0.73      5615<br />weighted avg       0.90      0.91      0.90      5615                    |

# 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]
```

# citation_bibtex

bibtex
@inproceedings{...,year={2024}}

# get_started_code

import skops.io as sio 
model = sio.load(file, trusted=unknown_types)

# model_card_authors

Smruti Padhy

# limitations

This model is ready to be used in production.

# model_description

This is a  Histogram-based Gradient Boosting Classification Tree model trained on HPC history jobs between 1Feb-1Aug 2022, window number1

# eval_method

The model is evaluated using test split, on accuracy and F1 score with macro average.

# confusion_matrix

![confusion_matrix](confusion_matrix.png)