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, ...)

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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

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