Model description
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Intended uses & limitations
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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 |
</details>
Model Plot
The model plot is below.
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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
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Citation
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model_card_authors
Moro abdul Wahab
model_description
ML classification model to predict or identify failures in a generator