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README.md

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+---
+library_name: sklearn
+tags:
+- sklearn
+- skops
+- tabular-regression
+model_file: pipeline.skops
+widget:
+  structuredData:
+    acceleration:
+    - 20.7
+    - 17.0
+    - 18.6
+    cylinders:
+    - 4
+    - 4
+    - 4
+    displacement:
+    - 98.0
+    - 120.0
+    - 120.0
+    horsepower:
+    - '65'
+    - '88'
+    - '79'
+    model year:
+    - 81
+    - 75
+    - 82
+    origin:
+    - 1
+    - 2
+    - 1
+    weight:
+    - 2380
+    - 2957
+    - 2625
+---
+
+# Model description
+
+This is a regression model on MPG dataset trained for this [kaggle tutorial](https://www.kaggle.com/unofficialmerve/persisting-your-scikit-learn-model-using-skops/).
+
+## 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         |
+|--------------------------|---------------|
+| ccp_alpha                | 0.0           |
+| criterion                | squared_error |
+| max_depth                |               |
+| max_features             |               |
+| max_leaf_nodes           |               |
+| min_impurity_decrease    | 0.0           |
+| min_samples_leaf         | 1             |
+| min_samples_split        | 2             |
+| min_weight_fraction_leaf | 0.0           |
+| random_state             |               |
+| splitter                 | best          |
+
+</details>
+
+### Model Plot
+
+The model plot is below.
+
+<style>#sk-3ea712fc-223a-4e18-9d66-e9fdc5d944b3 {color: black;background-color: white;}#sk-3ea712fc-223a-4e18-9d66-e9fdc5d944b3 pre{padding: 0;}#sk-3ea712fc-223a-4e18-9d66-e9fdc5d944b3 div.sk-toggleable {background-color: white;}#sk-3ea712fc-223a-4e18-9d66-e9fdc5d944b3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-3ea712fc-223a-4e18-9d66-e9fdc5d944b3 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-3ea712fc-223a-4e18-9d66-e9fdc5d944b3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-3ea712fc-223a-4e18-9d66-e9fdc5d944b3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-3ea712fc-223a-4e18-9d66-e9fdc5d944b3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-3ea712fc-223a-4e18-9d66-e9fdc5d944b3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-3ea712fc-223a-4e18-9d66-e9fdc5d944b3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-3ea712fc-223a-4e18-9d66-e9fdc5d944b3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-3ea712fc-223a-4e18-9d66-e9fdc5d944b3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-3ea712fc-223a-4e18-9d66-e9fdc5d944b3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-3ea712fc-223a-4e18-9d66-e9fdc5d944b3 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-3ea712fc-223a-4e18-9d66-e9fdc5d944b3 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-3ea712fc-223a-4e18-9d66-e9fdc5d944b3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-3ea712fc-223a-4e18-9d66-e9fdc5d944b3 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-3ea712fc-223a-4e18-9d66-e9fdc5d944b3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-3ea712fc-223a-4e18-9d66-e9fdc5d944b3 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-3ea712fc-223a-4e18-9d66-e9fdc5d944b3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-3ea712fc-223a-4e18-9d66-e9fdc5d944b3 div.sk-item {z-index: 1;}#sk-3ea712fc-223a-4e18-9d66-e9fdc5d944b3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-3ea712fc-223a-4e18-9d66-e9fdc5d944b3 div.sk-parallel::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-3ea712fc-223a-4e18-9d66-e9fdc5d944b3 div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-3ea712fc-223a-4e18-9d66-e9fdc5d944b3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-3ea712fc-223a-4e18-9d66-e9fdc5d944b3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-3ea712fc-223a-4e18-9d66-e9fdc5d944b3 div.sk-parallel-item:only-child::after {width: 0;}#sk-3ea712fc-223a-4e18-9d66-e9fdc5d944b3 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-3ea712fc-223a-4e18-9d66-e9fdc5d944b3 div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-3ea712fc-223a-4e18-9d66-e9fdc5d944b3 div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-3ea712fc-223a-4e18-9d66-e9fdc5d944b3 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-3ea712fc-223a-4e18-9d66-e9fdc5d944b3 div.sk-text-repr-fallback {display: none;}</style><div id="sk-3ea712fc-223a-4e18-9d66-e9fdc5d944b3" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>DecisionTreeRegressor()</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="37ade0f5-01f0-4181-acab-e7150c3b5fa2" type="checkbox" checked><label for="37ade0f5-01f0-4181-acab-e7150c3b5fa2" class="sk-toggleable__label sk-toggleable__label-arrow">DecisionTreeRegressor</label><div class="sk-toggleable__content"><pre>DecisionTreeRegressor()</pre></div></div></div></div></div>
+
+## Evaluation Results
+
+You can find the details about evaluation process and the evaluation results.
+
+
+
+| Metric             | Value                                 |
+|--------------------|---------------------------------------|
+| Mean Squared Error | 10.86399394359616                     |
+| R-Squared          | <function r2_score at 0x7f743fc54b00> |
+
+# How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+```python
+from skops.io import load
+import json
+import pandas as pd
+clf = load("pipeline.skops")
+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

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+{
+    "sklearn": {
+        "columns": [
+            "cylinders",
+            "displacement",
+            "horsepower",
+            "weight",
+            "acceleration",
+            "model year",
+            "origin"
+        ],
+        "environment": [
+            "scikit-learn"
+        ],
+        "example_input": {
+            "acceleration": [
+                20.7,
+                17.0,
+                18.6
+            ],
+            "cylinders": [
+                4,
+                4,
+                4
+            ],
+            "displacement": [
+                98.0,
+                120.0,
+                120.0
+            ],
+            "horsepower": [
+                "65",
+                "88",
+                "79"
+            ],
+            "model year": [
+                81,
+                75,
+                82
+            ],
+            "origin": [
+                1,
+                2,
+                1
+            ],
+            "weight": [
+                2380,
+                2957,
+                2625
+            ]
+        },
+        "model": {
+            "file": "pipeline.skops"
+        },
+        "task": "tabular-regression"
+    }
+}