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ml-generation-failure-prediction
moro23/ml-generation-failure-prediction
ml-generation-failure-prediction is a tabular classification model from moro23. 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.
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Updated Dec 17, 2022
Repo size
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At a glance
- Task
- Tabular Classification
- Library
- sklearn
- Access
- Public
- Created
- Dec 17, 2022
- Updated
- Dec 17, 2022
- SHA
- d1186864
Model card
From the Hugging Face model README
Model description
[More Information Needed]
Intended uses & limitations
[More Information Needed]
Training Procedure
Hyperparameters
The model is trained with below hyperparameters.
<details> <summary> Click to expand </summary>| Hyperparameter | Value |
|---|---|
| objective | binary:logistic |
| use_label_encoder | |
| base_score | 0.5 |
| booster | gbtree |
| callbacks | |
| colsample_bylevel | 1 |
| colsample_bynode | 1 |
| colsample_bytree | 1 |
| early_stopping_rounds | |
| enable_categorical | False |
| eval_metric | logloss |
| feature_types | |
| gamma | 3 |
| gpu_id | -1 |
| grow_policy | depthwise |
| importance_type | |
| interaction_constraints | |
| learning_rate | 0.1 |
| max_bin | 256 |
| max_cat_threshold | 64 |
| max_cat_to_onehot | 4 |
| max_delta_step | 0 |
| max_depth | 6 |
| max_leaves | 0 |
| min_child_weight | 1 |
| missing | nan |
| monotone_constraints | () |
| n_estimators | 250 |
| n_jobs | 0 |
| num_parallel_tree | 1 |
| predictor | auto |
| random_state | 1 |
| reg_alpha | 0 |
| reg_lambda | 1 |
| sampling_method | uniform |
| scale_pos_weight | 10 |
| subsample | 0.8 |
| tree_method | exact |
| validate_parameters | 1 |
| verbosity |
Model Plot
The model plot is below.
<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 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-2 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 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-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 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-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 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-2 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-2" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>XGBClassifier(base_score=0.5, booster='gbtree', callbacks=None,colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,early_stopping_rounds=None, enable_categorical=False,eval_metric='logloss', feature_types=None, gamma=3, gpu_id=-1,grow_policy='depthwise', importance_type=None,interaction_constraints='', learning_rate=0.1, max_bin=256,max_cat_threshold=64, max_cat_to_onehot=4, max_delta_step=0,max_depth=6, max_leaves=0, min_child_weight=1, missing=nan,monotone_constraints='()', n_estimators=250, n_jobs=0,num_parallel_tree=1, predictor='auto', random_state=1, ...)</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"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-2" type="checkbox" checked><label for="sk-estimator-id-2" class="sk-toggleable__label sk-toggleable__label-arrow">XGBClassifier</label><div class="sk-toggleable__content"><pre>XGBClassifier(base_score=0.5, booster='gbtree', callbacks=None,colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,early_stopping_rounds=None, enable_categorical=False,eval_metric='logloss', feature_types=None, gamma=3, gpu_id=-1,grow_policy='depthwise', importance_type=None,interaction_constraints='', learning_rate=0.1, max_bin=256,max_cat_threshold=64, max_cat_to_onehot=4, max_delta_step=0,max_depth=6, max_leaves=0, min_child_weight=1, missing=nan,monotone_constraints='()', n_estimators=250, n_jobs=0,num_parallel_tree=1, predictor='auto', random_state=1, ...)</pre></div></div></div></div></div>Evaluation Results
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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: [More Information Needed]
Citation
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BibTeX:
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model_card_authors
Moro abdul Wahab
model_description
ML classification model to predict or identify failures in a generator
- Task
- Tabular Classification
- Library
- sklearn
- Created
- Dec 17, 2022
- Updated
- Dec 17, 2022