Alindstroem89/stroke_predicting_model

Model description

The model is intended to be used to predict if a person is likely to get a stroke or not

Intended uses & limitations

[More Information Needed]

Training Procedure

[More Information Needed]

Hyperparameters

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Hyperparameter	Value
objective	binary:logistic
base_score
booster
callbacks
colsample_bylevel	0.9076228511174643
colsample_bynode
colsample_bytree	0.8045246933821307
device
early_stopping_rounds
enable_categorical	False
eval_metric
feature_types
gamma
grow_policy
importance_type
interaction_constraints
learning_rate	0.0711965541329635
max_bin
max_cat_threshold
max_cat_to_onehot
max_delta_step
max_depth
max_leaves	4
min_child_weight	0.27994747825685384
missing	nan
monotone_constraints
multi_strategy
n_estimators	35
n_jobs
num_parallel_tree
random_state
reg_alpha	0.0009765625
reg_lambda	2.991485993669717
sampling_method
scale_pos_weight
subsample	0.8073913094722203
tree_method
validate_parameters
verbosity

Model Plot

XGBClassifier(base_score=None, booster=None, callbacks=None,colsample_bylevel=0.9076228511174643, colsample_bynode=None,colsample_bytree=0.8045246933821307, device=None,early_stopping_rounds=None, enable_categorical=False,eval_metric=None, feature_types=None, gamma=None,grow_policy=None, importance_type=None,interaction_constraints=None, learning_rate=0.0711965541329635,max_bin=None, max_cat_threshold=None, max_cat_to_onehot=None,max_delta_step=None, max_depth=None, max_leaves=4,min_child_weight=0.27994747825685384, missing=nan,monotone_constraints=None, multi_strategy=None, n_estimators=35,n_jobs=None, num_parallel_tree=None, random_state=None, ...)

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Evaluation Results

Metric	Value
accuracy	0.78

Confusion Matrix

How to Get Started with the Model

[More Information Needed]

Model Card Authors

Alexander Lindström

Model Card Contact

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Citation

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BibTeX:

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precision recall f1-score support

 class 0       0.98      0.78      0.87       960
 class 1       0.18      0.76      0.29        62

accuracy                           0.78      1022

macro avg 0.58 0.77 0.58 1022 weighted avg 0.93 0.78 0.83 1022

Metric	Value
accuracy	0.78