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
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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))])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. Evaluation Results
Metric Value accuracy 0.946168166304685 classification report precision recall f1-score support
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
You can contact the model card authors through following channels:
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Citation
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BibTeX:
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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
confusion_matrix 
