--- 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
Click to expand | 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 | 0.946168166304685 | | classification report | precision recall f1-score support

0 0.97 0.98 0.98 5075
1 0.74 0.57 0.64 218
2 0.70 0.59 0.64 108
3 0.67 0.55 0.60 86
4 0.89 0.92 0.90 959

accuracy 0.95 6446
macro avg 0.79 0.72 0.75 6446
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 ![confusion_matrix](confusion_matrix.png)