smart-ml-hpc-hug0

smpadhy/smart-ml-hpc-hug0

smart-ml-hpc-hug0 is a tabular classification model from smpadhy. Use it for the tabular classification task on the model card, and read the license before you ship it in a product. It is set up for sklearn. The card lists the license as mit.

This is a Histogram-based Gradient Boosting Classification Tree model trained on HPC history jobs between 1Feb-1Aug 2022, window number 0.

Downloads · 30 days

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Public

Updated Jul 24, 2024

Repo size

2.7 MB

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Public

Hugging Face

Repo makeup

Click a slice to open those files.

.pkl2.7 MB · 99%

At a glance

Task: Tabular Classification
Library: sklearn
License: mit
Access: Public
Created: Jul 24, 2024
Updated: Jul 24, 2024
SHA: 4210cd74

Spaces

smpadhy/ml-hpc

Model card

From the Hugging Face model README

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

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

Task: Tabular Classification
Library: sklearn
License: mit
Created: Jul 24, 2024
Updated: Jul 24, 2024