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Model description

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Intended uses & limitations

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Training Procedure

Hyperparameters

The model is trained with below hyperparameters.

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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.

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, ...)
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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

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