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---
library_name: sklearn
tags:
- sklearn
- skops
- tabular-classification
model_file: model.pkl
widget:
structuredData:
area_mean:
- 407.4
- 1335.0
- 428.0
area_se:
- 26.99
- 77.02
- 17.12
area_worst:
- 508.9
- 1946.0
- 546.3
compactness_mean:
- 0.05991
- 0.1076
- 0.069
compactness_se:
- 0.01065
- 0.01895
- 0.01727
compactness_worst:
- 0.1049
- 0.3055
- 0.188
concave points_mean:
- 0.02069
- 0.08941
- 0.01393
concave points_se:
- 0.009175
- 0.01232
- 0.006747
concave points_worst:
- 0.06544
- 0.2112
- 0.06913
concavity_mean:
- 0.02638
- 0.1527
- 0.02669
concavity_se:
- 0.01245
- 0.02681
- 0.02045
concavity_worst:
- 0.08105
- 0.4159
- 0.1471
fractal_dimension_mean:
- 0.05934
- 0.05478
- 0.06057
fractal_dimension_se:
- 0.001461
- 0.001711
- 0.002922
fractal_dimension_worst:
- 0.06487
- 0.07055
- 0.07993
perimeter_mean:
- 73.28
- 134.8
- 75.51
perimeter_se:
- 2.684
- 4.119
- 1.444
perimeter_worst:
- 83.12
- 166.8
- 85.22
radius_mean:
- 11.5
- 20.64
- 11.84
radius_se:
- 0.3927
- 0.6137
- 0.2222
radius_worst:
- 12.97
- 25.37
- 13.3
smoothness_mean:
- 0.09345
- 0.09446
- 0.08871
smoothness_se:
- 0.00638
- 0.006211
- 0.005517
smoothness_worst:
- 0.1183
- 0.1562
- 0.128
symmetry_mean:
- 0.1834
- 0.1571
- 0.1533
symmetry_se:
- 0.02292
- 0.01276
- 0.01616
symmetry_worst:
- 0.274
- 0.2689
- 0.2535
texture_mean:
- 18.45
- 17.35
- 18.94
texture_se:
- 0.8429
- 0.6575
- 0.8652
texture_worst:
- 22.46
- 23.17
- 24.99
---
# Model description
This is a Logistic Regression trained on breast cancer dataset.
## Intended uses & limitations
This model is trained for educational purposes.
## Training Procedure
### Hyperparameters
The model is trained with below hyperparameters.
<details>
<summary> Click to expand </summary>
| Hyperparameter | Value |
|--------------------------|-----------------------------------------------------------------|
| memory | |
| steps | [('scaler', StandardScaler()), ('model', LogisticRegression())] |
| verbose | False |
| scaler | StandardScaler() |
| model | LogisticRegression() |
| 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-5b6643ea-0cef-4d0c-8389-2cf071bf6152 {color: black;background-color: white;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 pre{padding: 0;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 div.sk-toggleable {background-color: white;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 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-5b6643ea-0cef-4d0c-8389-2cf071bf6152 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-5b6643ea-0cef-4d0c-8389-2cf071bf6152 div.sk-estimator:hover {background-color: #d4ebff;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 div.sk-item {z-index: 1;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 div.sk-parallel::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 div.sk-parallel-item:only-child::after {width: 0;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 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-5b6643ea-0cef-4d0c-8389-2cf071bf6152 div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 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-5b6643ea-0cef-4d0c-8389-2cf071bf6152 div.sk-text-repr-fallback {display: none;}</style><div id="sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[('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="76a688ab-e260-4cf7-a9f2-bf77900be27c" type="checkbox" ><label for="76a688ab-e260-4cf7-a9f2-bf77900be27c" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[('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="6a4fcd10-6b63-40a6-a848-13717b9f7c82" type="checkbox" ><label for="6a4fcd10-6b63-40a6-a848-13717b9f7c82" 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="974bd93d-19db-4a61-b7ff-66d07e5bbadb" type="checkbox" ><label for="974bd93d-19db-4a61-b7ff-66d07e5bbadb" 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 | 0.965035 |
| f1 score | 0.965035 |
# How to Get Started with the Model
Use the code below to get started with the model.
```python
import joblib
import json
import pandas as pd
clf = joblib.load(model.pkl)
with open("config.json") as f:
config = json.load(f)
clf.predict(pd.DataFrame.from_dict(config["sklearn"]["example_input"]))
```
# Additional Content
## Confusion Matrix
 |