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README.md

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+---
+library_name: sklearn
+tags:
+- sklearn
+- skops
+- tabular-regression
+model_file: skops47mqlzp0
+widget:
+  structuredData:
+    acceleration:
+    - 12.0
+    - 19.0
+    - 20.7
+    cylinders:
+    - 8
+    - 4
+    - 4
+    displacement:
+    - 307.0
+    - 97.0
+    - 98.0
+    horsepower:
+    - '130'
+    - '88'
+    - '65'
+    model year:
+    - 70
+    - 73
+    - 81
+    origin:
+    - 1
+    - 3
+    - 1
+    weight:
+    - 3504
+    - 2279
+    - 2380
+---
+
+# Model description
+
+This is a passive-agressive regression model used for continuous training. Find the notebook [here](https://www.kaggle.com/code/unofficialmerve/incremental-online-training-with-scikit-learn/)
+
+## Intended uses & limitations
+
+This model is not ready to be used in production. It's trained to predict MPG a car spends based on it's attributes.
+
+## Training Procedure
+
+### Hyperparameters
+
+The model is trained with below hyperparameters.
+
+<details>
+<summary> Click to expand </summary>
+
+| Hyperparameter      | Value               |
+|---------------------|---------------------|
+| C                   | 0.01                |
+| average             | False               |
+| early_stopping      | False               |
+| epsilon             | 0.1                 |
+| fit_intercept       | True                |
+| loss                | epsilon_insensitive |
+| max_iter            | 1000                |
+| n_iter_no_change    | 5                   |
+| random_state        |                     |
+| shuffle             | True                |
+| tol                 | 0.001               |
+| validation_fraction | 0.1                 |
+| verbose             | 0                   |
+| warm_start          | False               |
+
+</details>
+
+### Model Plot
+
+The model plot is below.
+
+<style>#sk-1c3ea46c-0796-439d-856b-fedc4a20d47e {color: black;background-color: white;}#sk-1c3ea46c-0796-439d-856b-fedc4a20d47e pre{padding: 0;}#sk-1c3ea46c-0796-439d-856b-fedc4a20d47e div.sk-toggleable {background-color: white;}#sk-1c3ea46c-0796-439d-856b-fedc4a20d47e label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-1c3ea46c-0796-439d-856b-fedc4a20d47e label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-1c3ea46c-0796-439d-856b-fedc4a20d47e label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-1c3ea46c-0796-439d-856b-fedc4a20d47e div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-1c3ea46c-0796-439d-856b-fedc4a20d47e div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-1c3ea46c-0796-439d-856b-fedc4a20d47e div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-1c3ea46c-0796-439d-856b-fedc4a20d47e input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-1c3ea46c-0796-439d-856b-fedc4a20d47e input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-1c3ea46c-0796-439d-856b-fedc4a20d47e div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-1c3ea46c-0796-439d-856b-fedc4a20d47e div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-1c3ea46c-0796-439d-856b-fedc4a20d47e 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-1c3ea46c-0796-439d-856b-fedc4a20d47e 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-1c3ea46c-0796-439d-856b-fedc4a20d47e div.sk-estimator:hover {background-color: #d4ebff;}#sk-1c3ea46c-0796-439d-856b-fedc4a20d47e div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-1c3ea46c-0796-439d-856b-fedc4a20d47e div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-1c3ea46c-0796-439d-856b-fedc4a20d47e div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-1c3ea46c-0796-439d-856b-fedc4a20d47e div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-1c3ea46c-0796-439d-856b-fedc4a20d47e div.sk-item {z-index: 1;}#sk-1c3ea46c-0796-439d-856b-fedc4a20d47e div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-1c3ea46c-0796-439d-856b-fedc4a20d47e div.sk-parallel::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-1c3ea46c-0796-439d-856b-fedc4a20d47e div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-1c3ea46c-0796-439d-856b-fedc4a20d47e div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-1c3ea46c-0796-439d-856b-fedc4a20d47e div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-1c3ea46c-0796-439d-856b-fedc4a20d47e div.sk-parallel-item:only-child::after {width: 0;}#sk-1c3ea46c-0796-439d-856b-fedc4a20d47e 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-1c3ea46c-0796-439d-856b-fedc4a20d47e div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-1c3ea46c-0796-439d-856b-fedc4a20d47e div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-1c3ea46c-0796-439d-856b-fedc4a20d47e 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-1c3ea46c-0796-439d-856b-fedc4a20d47e div.sk-text-repr-fallback {display: none;}</style><div id="sk-1c3ea46c-0796-439d-856b-fedc4a20d47e" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>PassiveAggressiveRegressor(C=0.01)</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"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="13f821ce-da7c-4825-b16d-1394a33b5711" type="checkbox" checked><label for="13f821ce-da7c-4825-b16d-1394a33b5711" class="sk-toggleable__label sk-toggleable__label-arrow">PassiveAggressiveRegressor</label><div class="sk-toggleable__content"><pre>PassiveAggressiveRegressor(C=0.01)</pre></div></div></div></div></div>
+
+## Evaluation Results
+
+You can find the details about evaluation process and the evaluation results.
+
+
+
+| Metric   | Value   |
+|----------|---------|
+
+# 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(skops47mqlzp0)
+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]
+```

config.json

@@ -0,0 +1,57 @@
+{
+    "sklearn": {
+        "columns": [
+            "cylinders",
+            "displacement",
+            "horsepower",
+            "weight",
+            "acceleration",
+            "model year",
+            "origin"
+        ],
+        "environment": [
+            "scikit-learn"
+        ],
+        "example_input": {
+            "acceleration": [
+                12.0,
+                19.0,
+                20.7
+            ],
+            "cylinders": [
+                8,
+                4,
+                4
+            ],
+            "displacement": [
+                307.0,
+                97.0,
+                98.0
+            ],
+            "horsepower": [
+                "130",
+                "88",
+                "65"
+            ],
+            "model year": [
+                70,
+                73,
+                81
+            ],
+            "origin": [
+                1,
+                3,
+                1
+            ],
+            "weight": [
+                3504,
+                2279,
+                2380
+            ]
+        },
+        "model": {
+            "file": "skops47mqlzp0"
+        },
+        "task": "tabular-regression"
+    }
+}