smpadhy/smart-ml-hpc-hug1

Model description

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Intended uses & limitations

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Training Procedure

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Hyperparameters

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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

Model Plot

Pipeline(steps=[('scale', StandardScaler()),('hgbc',HistGradientBoostingClassifier(max_depth=9, max_iter=600))])

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Evaluation Results

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

How to Get Started with the Model

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Model Card Authors

This model card is written by following authors:

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Model Card Contact

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Citation

Below you can find information related to citation.

BibTeX:

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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