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---
license: mit
library_name: sklearn
tags:
- sklearn
- skops
- tabular-classification
model_file: example.pkl
widget:
structuredData:
'Unnamed: 32':
- .nan
- .nan
- .nan
area_mean:
- 481.9
- 1130.0
- 748.9
area_se:
- 30.29
- 96.05
- 48.31
area_worst:
- 677.9
- 1866.0
- 1156.0
compactness_mean:
- 0.1058
- 0.1029
- 0.1223
compactness_se:
- 0.01911
- 0.01652
- 0.01484
compactness_worst:
- 0.2378
- 0.2336
- 0.2394
concave points_mean:
- 0.03821
- 0.07951
- 0.08087
concave points_se:
- 0.01037
- 0.0137
- 0.01093
concave points_worst:
- 0.1015
- 0.1789
- 0.1514
concavity_mean:
- 0.08005
- 0.108
- 0.1466
concavity_se:
- 0.02701
- 0.02269
- 0.02813
concavity_worst:
- 0.2671
- 0.2687
- 0.3791
fractal_dimension_mean:
- 0.06373
- 0.05461
- 0.05796
fractal_dimension_se:
- 0.003586
- 0.001698
- 0.002461
fractal_dimension_worst:
- 0.0875
- 0.06589
- 0.08019
id:
- 87930
- 859575
- 8670
perimeter_mean:
- 81.09
- 123.6
- 101.7
perimeter_se:
- 2.497
- 5.486
- 3.094
perimeter_worst:
- 96.05
- 165.9
- 124.9
radius_mean:
- 12.47
- 18.94
- 15.46
radius_se:
- 0.3961
- 0.7888
- 0.4743
radius_worst:
- 14.97
- 24.86
- 19.26
smoothness_mean:
- 0.09965
- 0.09009
- 0.1092
smoothness_se:
- 0.006953
- 0.004444
- 0.00624
smoothness_worst:
- 0.1426
- 0.1193
- 0.1546
symmetry_mean:
- 0.1925
- 0.1582
- 0.1931
symmetry_se:
- 0.01782
- 0.01386
- 0.01397
symmetry_worst:
- 0.3014
- 0.2551
- 0.2837
texture_mean:
- 18.6
- 21.31
- 19.48
texture_se:
- 1.044
- 0.7975
- 0.7859
texture_worst:
- 24.64
- 26.58
- 26.0
---
# Model description
[More Information Needed]
## Intended uses & limitations
This model is not ready to be used in production.
## Training Procedure
### Hyperparameters
The model is trained with below hyperparameters.
<details>
<summary> Click to expand </summary>
| Hyperparameter | Value |
|--------------------------|-----------------------------------------------------------------------------------------------|
| memory | |
| steps | [('imputer', SimpleImputer()), ('scaler', StandardScaler()), ('model', LogisticRegression())] |
| verbose | False |
| imputer | SimpleImputer() |
| scaler | StandardScaler() |
| model | LogisticRegression() |
| imputer__add_indicator | False |
| imputer__copy | True |
| imputer__fill_value | |
| imputer__missing_values | nan |
| imputer__strategy | mean |
| imputer__verbose | 0 |
| scaler__copy | True |
| scaler__with_mean | True |
| scaler__with_std | True |
| model__C | 1.0 |
| model__class_weight | |
| model__dual | False |
| model__fit_intercept | True |
| model__intercept_scaling | 1 |
| model__l1_ratio | |
| model__max_iter | 100 |
| model__multi_class | auto |
| model__n_jobs | |
| model__penalty | l2 |
| model__random_state | |
| model__solver | lbfgs |
| model__tol | 0.0001 |
| model__verbose | 0 |
| model__warm_start | False |
</details>
### Model Plot
The model plot is below.
<style>#sk-e60317e1-ee5c-4f4d-98a6-92332ba1474b {color: black;background-color: white;}#sk-e60317e1-ee5c-4f4d-98a6-92332ba1474b pre{padding: 0;}#sk-e60317e1-ee5c-4f4d-98a6-92332ba1474b div.sk-toggleable {background-color: white;}#sk-e60317e1-ee5c-4f4d-98a6-92332ba1474b label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-e60317e1-ee5c-4f4d-98a6-92332ba1474b label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-e60317e1-ee5c-4f4d-98a6-92332ba1474b label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-e60317e1-ee5c-4f4d-98a6-92332ba1474b div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-e60317e1-ee5c-4f4d-98a6-92332ba1474b div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-e60317e1-ee5c-4f4d-98a6-92332ba1474b div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-e60317e1-ee5c-4f4d-98a6-92332ba1474b input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-e60317e1-ee5c-4f4d-98a6-92332ba1474b input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-e60317e1-ee5c-4f4d-98a6-92332ba1474b div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-e60317e1-ee5c-4f4d-98a6-92332ba1474b div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-e60317e1-ee5c-4f4d-98a6-92332ba1474b 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-e60317e1-ee5c-4f4d-98a6-92332ba1474b 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-e60317e1-ee5c-4f4d-98a6-92332ba1474b div.sk-estimator:hover {background-color: #d4ebff;}#sk-e60317e1-ee5c-4f4d-98a6-92332ba1474b div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-e60317e1-ee5c-4f4d-98a6-92332ba1474b div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-e60317e1-ee5c-4f4d-98a6-92332ba1474b div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-e60317e1-ee5c-4f4d-98a6-92332ba1474b div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-e60317e1-ee5c-4f4d-98a6-92332ba1474b div.sk-item {z-index: 1;}#sk-e60317e1-ee5c-4f4d-98a6-92332ba1474b div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-e60317e1-ee5c-4f4d-98a6-92332ba1474b div.sk-parallel::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-e60317e1-ee5c-4f4d-98a6-92332ba1474b div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-e60317e1-ee5c-4f4d-98a6-92332ba1474b div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-e60317e1-ee5c-4f4d-98a6-92332ba1474b div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-e60317e1-ee5c-4f4d-98a6-92332ba1474b div.sk-parallel-item:only-child::after {width: 0;}#sk-e60317e1-ee5c-4f4d-98a6-92332ba1474b 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;position: relative;}#sk-e60317e1-ee5c-4f4d-98a6-92332ba1474b div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-e60317e1-ee5c-4f4d-98a6-92332ba1474b div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-e60317e1-ee5c-4f4d-98a6-92332ba1474b 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-e60317e1-ee5c-4f4d-98a6-92332ba1474b div.sk-text-repr-fallback {display: none;}</style><div id="sk-e60317e1-ee5c-4f4d-98a6-92332ba1474b" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[('imputer', SimpleImputer()), ('scaler', StandardScaler()),('model', LogisticRegression())])</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="6aee50d2-d0d7-437e-8e9b-bd1121de94e7" type="checkbox" ><label for="6aee50d2-d0d7-437e-8e9b-bd1121de94e7" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[('imputer', SimpleImputer()), ('scaler', StandardScaler()),('model', LogisticRegression())])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="ac5b7f88-9a16-4c90-8fcb-2a4f833cadf1" type="checkbox" ><label for="ac5b7f88-9a16-4c90-8fcb-2a4f833cadf1" class="sk-toggleable__label sk-toggleable__label-arrow">SimpleImputer</label><div class="sk-toggleable__content"><pre>SimpleImputer()</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="65ce6721-e323-4189-a9bd-e373e248f0f7" type="checkbox" ><label for="65ce6721-e323-4189-a9bd-e373e248f0f7" class="sk-toggleable__label sk-toggleable__label-arrow">StandardScaler</label><div class="sk-toggleable__content"><pre>StandardScaler()</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="2328c6c4-413e-46ed-b597-1b88227e45a5" type="checkbox" ><label for="2328c6c4-413e-46ed-b597-1b88227e45a5" class="sk-toggleable__label sk-toggleable__label-arrow">LogisticRegression</label><div class="sk-toggleable__content"><pre>LogisticRegression()</pre></div></div></div></div></div></div></div>
## Evaluation Results
You can find the details about evaluation process and the evaluation results.
| Metric | Value |
|----------|----------|
| accuracy | 0.982456 |
| f1 score | 0.982456 |
# How to Get Started with the Model
[More Information Needed]
# Model Card Authors
This model card is written by following authors:
[More Information Needed]
# 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]
```
# Confusion Matrix
|