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---
library_name: sklearn
license: mit
tags:
- sklearn
- skops
- tabular-classification
model_format: pickle
model_file: febskxmodel_hug_0.pkl
widget:
- structuredData:
backlog_minutes:
- 793051
- 474385
- 785116
backlog_num_jobs:
- 302
- 193
- 302
max_minutes:
- 18
- 360
- 18
nnodes:
- 1
- 1
- 1
running_minutes:
- 1934034
- 1934094
- 1934034
running_num_jobs:
- 6827
- 6828
- 6827
---
# Model description
This is a Histogram-based Gradient Boosting Classification Tree model trained on HPC history jobs between 1Feb-1Aug 2022, window number 0.
Window Start: 2022-02-01 00:06:58; Window End: 2022-03-03 04:05:20; Total Jobs in Window 0: 35812.
Best parameters: {'hgbc__learning_rate': 0.1, 'hgbc__max_depth': 9, 'hgbc__max_iter': 600}
Performance on TEST
Accuracy on entire set: 0.946168166304685
Accuracy for last bin scheduling assuming bins <= 0 are incorrect: 0.9454; (936/990)
Accuracy for last bin scheduling assuming bins <= 1 are incorrect: 0.9242; (915/990)
Accuracy for last bin scheduling assuming bins <= 2 are incorrect: 0.9121; (903/990)
Accuracy for last bin scheduling assuming bins <= 3 are incorrect: 0.8878; (879/990)
## 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=[('scale', StandardScaler()),('hgbc',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=[('scale', StandardScaler()),('hgbc',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 | 0.946168166304685 |
| classification report | precision recall f1-score support<br /><br /> 0 0.97 0.98 0.98 5075<br /> 1 0.74 0.57 0.64 218<br /> 2 0.70 0.59 0.64 108<br /> 3 0.67 0.55 0.60 86<br /> 4 0.89 0.92 0.90 959<br /><br /> accuracy 0.95 6446<br /> macro avg 0.79 0.72 0.75 6446<br />weighted avg 0.94 0.95 0.94 6446 |
# 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 pickle
with open(dtc_pkl_filename, 'rb') as file:
clf = pickle.load(file)
# model_card_authors
Smruti Padhy
Joe Stubbs
# 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 number0
# eval_method
The model is evaluated using test split, on accuracy and F1 score with macro average.
# confusion_matrix
|