metadata
library_name: sklearn
tags:
- sklearn
- skops
- tabular-classification
model_format: pickle
model_file: stroke_model.pkl
widget:
- structuredData:
Residence_type_Rural:
- true
- true
- false
Residence_type_Urban:
- false
- false
- true
age:
- 0.15771484375
- 0.7802734375
- 0.31640625
avg_glucose_level:
- 0.24563752192779986
- 0.3366263502908319
- 0.04413258240236362
avg_glucose_level/bmi:
- 0.35152096177583636
- 0.18922222093200816
- 0.12391183202584002
bmi:
- 0.10487444608567206
- 0.35007385524372225
- 0.1920236336779911
ever_married_No:
- true
- false
- true
ever_married_Yes:
- false
- true
- false
gender_Female:
- false
- true
- false
gender_Male:
- true
- false
- true
gender_Other:
- false
- false
- false
heart_disease_No:
- true
- true
- true
heart_disease_Yes:
- false
- false
- false
hypertension_No:
- true
- true
- true
hypertension_Yes:
- false
- false
- false
smoking_status_Unknown:
- false
- false
- false
smoking_status_formerly smoked:
- false
- false
- false
smoking_status_never smoked:
- true
- false
- false
smoking_status_smokes:
- false
- true
- true
work_type_Govt_job:
- false
- false
- false
work_type_Never_worked:
- false
- false
- false
work_type_Private:
- false
- false
- true
work_type_Self-employed:
- false
- true
- false
work_type_children:
- true
- false
- false
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
Click to expand
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, ...)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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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, ...)
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
You can contact the model card authors through following channels: [More Information Needed]
Citation
Below you can find information related to citation.
BibTeX:
[More Information Needed]
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 |