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---
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
<details>
<summary> Click to expand </summary>
| 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 | |
</details>
### Model Plot
<style>#sk-container-id-23 {color: black;}#sk-container-id-23 pre{padding: 0;}#sk-container-id-23 div.sk-toggleable {background-color: white;}#sk-container-id-23 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-23 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-23 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-23 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-23 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-23 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-23 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-23 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-23 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-23 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-23 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-23 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-23 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-23 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-23 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-23 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-23 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-23 div.sk-item {position: relative;z-index: 1;}#sk-container-id-23 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-23 div.sk-item::before, #sk-container-id-23 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-23 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-23 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-23 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-23 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-23 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-23 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-23 div.sk-label-container {text-align: center;}#sk-container-id-23 div.sk-container {/* jupyter's `normalize.less` 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-23 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-23" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>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, ...)</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-23" type="checkbox" checked><label for="sk-estimator-id-23" class="sk-toggleable__label sk-toggleable__label-arrow">XGBClassifier</label><div class="sk-toggleable__content"><pre>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, ...)</pre></div></div></div></div></div>
## 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 |
|