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README.md

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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=[(&#x27;scaler&#x27;, StandardScaler()), (&#x27;model&#x27;, 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=[(&#x27;scaler&#x27;, StandardScaler()), (&#x27;model&#x27;, 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"]))
+```
+
+
+# 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]
+```
+
+
+# Additional Content
+
+## Confusion Matrix
+
+![Confusion Matrix](confusion_matrix.png)

config.json

@@ -0,0 +1,195 @@
+{
+    "sklearn": {
+        "columns": [
+            "radius_mean",
+            "texture_mean",
+            "perimeter_mean",
+            "area_mean",
+            "smoothness_mean",
+            "compactness_mean",
+            "concavity_mean",
+            "concave points_mean",
+            "symmetry_mean",
+            "fractal_dimension_mean",
+            "radius_se",
+            "texture_se",
+            "perimeter_se",
+            "area_se",
+            "smoothness_se",
+            "compactness_se",
+            "concavity_se",
+            "concave points_se",
+            "symmetry_se",
+            "fractal_dimension_se",
+            "radius_worst",
+            "texture_worst",
+            "perimeter_worst",
+            "area_worst",
+            "smoothness_worst",
+            "compactness_worst",
+            "concavity_worst",
+            "concave points_worst",
+            "symmetry_worst",
+            "fractal_dimension_worst"
+        ],
+        "environment": [
+            "scikit-learn"
+        ],
+        "example_input": {
+            "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": {
+            "file": "model.pkl"
+        },
+        "task": "tabular-classification"
+    }
+}

model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:19e7603c2a00866878c72a1d61f9adc0aaa687f0ba70be71ecdd3e7f610ae9d4
+size 3374