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  1. README.md +152 -0
  2. config.json +52 -0
  3. confusion_matrix.png +0 -0
  4. febskxmodel_hug_0.pkl +3 -0
README.md ADDED
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+ ---
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+ library_name: sklearn
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+ license: mit
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+ tags:
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+ - sklearn
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+ - skops
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+ - tabular-classification
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+ model_format: pickle
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+ model_file: febskxmodel_hug_0.pkl
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+ widget:
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+ - structuredData:
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+ backlog_minutes:
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+ - 793051
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+ - 474385
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+ - 785116
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+ backlog_num_jobs:
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+ - 302
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+ - 193
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+ - 302
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+ max_minutes:
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+ - 18
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+ - 360
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+ - 18
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+ nnodes:
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+ - 1
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+ - 1
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+ - 1
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+ running_minutes:
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+ - 1934034
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+ - 1934094
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+ - 1934034
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+ running_num_jobs:
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+ - 6827
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+ - 6828
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+ - 6827
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+ ---
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+
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+ # Model description
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+
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+ [More Information Needed]
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+
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+ ## Intended uses & limitations
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+
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+ [More Information Needed]
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+
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+ ## Training Procedure
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+
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+ [More Information Needed]
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+
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+ ### Hyperparameters
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+
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+ <details>
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+ <summary> Click to expand </summary>
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+
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+ | Hyperparameter | Value |
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+ |----------------------------|----------------------------------------------------------------------------------------------------|
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+ | memory | |
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+ | steps | [('scale', StandardScaler()), ('hgbc', HistGradientBoostingClassifier(max_depth=9, max_iter=600))] |
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+ | verbose | False |
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+ | scale | StandardScaler() |
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+ | hgbc | HistGradientBoostingClassifier(max_depth=9, max_iter=600) |
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+ | scale__copy | True |
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+ | scale__with_mean | True |
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+ | scale__with_std | True |
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+ | hgbc__categorical_features | |
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+ | hgbc__class_weight | |
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+ | hgbc__early_stopping | auto |
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+ | hgbc__interaction_cst | |
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+ | hgbc__l2_regularization | 0.0 |
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+ | hgbc__learning_rate | 0.1 |
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+ | hgbc__loss | log_loss |
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+ | hgbc__max_bins | 255 |
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+ | hgbc__max_depth | 9 |
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+ | hgbc__max_iter | 600 |
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+ | hgbc__max_leaf_nodes | 31 |
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+ | hgbc__min_samples_leaf | 20 |
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+ | hgbc__monotonic_cst | |
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+ | hgbc__n_iter_no_change | 10 |
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+ | hgbc__random_state | |
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+ | hgbc__scoring | loss |
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+ | hgbc__tol | 1e-07 |
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+ | hgbc__validation_fraction | 0.1 |
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+ | hgbc__verbose | 0 |
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+ | hgbc__warm_start | False |
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+
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+ </details>
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+
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+ ### Model Plot
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+
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+ <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=[(&#x27;scale&#x27;, StandardScaler()),(&#x27;hgbc&#x27;,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=[(&#x27;scale&#x27;, StandardScaler()),(&#x27;hgbc&#x27;,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>
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+
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+ ## Evaluation Results
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+
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+ | Metric | Value |
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+ |-----------------------|-------------------|
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+ | accuracy | 0.946168166304685 |
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+ | 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 |
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+
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+ # How to Get Started with the Model
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+
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+ [More Information Needed]
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+
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+ # Model Card Authors
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+
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+ This model card is written by following authors:
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+
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+ [More Information Needed]
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+
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+ # Model Card Contact
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+
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+ You can contact the model card authors through following channels:
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+ [More Information Needed]
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+
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+ # Citation
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+
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+ Below you can find information related to citation.
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+
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+ **BibTeX:**
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+ ```
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+ [More Information Needed]
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+ ```
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+
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+ # citation_bibtex
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+
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+ bibtex
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+ @inproceedings{...,year={2024}}
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+
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+ # get_started_code
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+
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+ import pickle
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+ with open(dtc_pkl_filename, 'rb') as file:
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+ clf = pickle.load(file)
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+
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+ # model_card_authors
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+
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+ Smruti Padhy
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+
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+ # limitations
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+
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+ This model is ready to be used in production.
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+
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+ # model_description
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+
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+ This is a Histogram-based Gradient Boosting Classification Tree model trained on HPC history jobs between 1Feb-1Aug 2022, window number0
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+
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+ # eval_method
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+
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+ The model is evaluated using test split, on accuracy and F1 score with macro average.
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+
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+ # confusion_matrix
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+
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+ ![confusion_matrix](confusion_matrix.png)
config.json ADDED
@@ -0,0 +1,52 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ {
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+ "sklearn": {
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+ "columns": [
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+ "nnodes",
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+ "max_minutes",
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+ "backlog_minutes",
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+ "backlog_num_jobs",
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+ "running_num_jobs",
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+ "running_minutes"
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+ ],
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+ "environment": [
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+ "scikit-learn=1.2.2"
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+ ],
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+ "example_input": {
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+ "backlog_minutes": [
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+ 793051,
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+ 474385,
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+ 785116
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+ ],
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+ "backlog_num_jobs": [
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+ 302,
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+ 193,
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+ 302
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+ ],
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+ "max_minutes": [
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+ 18,
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+ 360,
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+ 18
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+ ],
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+ "nnodes": [
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+ 1,
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+ 1,
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+ 1
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+ ],
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+ "running_minutes": [
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+ 1934034,
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+ 1934094,
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+ 1934034
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+ ],
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+ "running_num_jobs": [
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+ 6827,
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+ 6828,
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+ 6827
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+ ]
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+ },
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+ "model": {
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+ "file": "febskxmodel_hug_0.pkl"
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+ },
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+ "model_format": "pickle",
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+ "task": "tabular-classification"
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+ }
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+ }
confusion_matrix.png ADDED
febskxmodel_hug_0.pkl ADDED
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