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Fish-Weight

Tabular Regression
Scikit-learn
skops
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  1. README.md +154 -0
  2. config.json +51 -0
  3. example.pkl +3 -0
README.md ADDED
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+ ---
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+ license: mit
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+ library_name: sklearn
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+ tags:
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+ - sklearn
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+ - skops
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+ - tabular-regression
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+ widget:
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+ structuredData:
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+ Height:
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+ - 11.52
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+ - 12.48
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+ - 12.3778
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+ Length1:
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+ - 23.2
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+ - 24.0
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+ - 23.9
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+ Length2:
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+ - 25.4
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+ - 26.3
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+ - 26.5
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+ Length3:
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+ - 30.0
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+ - 31.2
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+ - 31.1
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+ Species:
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+ - Bream
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+ - Bream
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+ - Bream
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+ Width:
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+ - 4.02
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+ - 4.3056
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+ - 4.6961
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+ ---
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+
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+ # Model description
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+
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+ This is a GradientBoostingRegressor on a fish dataset.
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+
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+ ## Intended uses & limitations
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+
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+ This model is intended for educational purposes.
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+
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+ ## Training Procedure
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+
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+ ### Hyperparameters
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+
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+ The model is trained with below hyperparameters.
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+
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+ <details>
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+ <summary> Click to expand </summary>
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+
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+ | Hyperparameter | Value |
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+ |-----------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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+ | memory | |
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+ | steps | [('columntransformer', ColumnTransformer(remainder='passthrough',
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+ transformers=[('onehotencoder',
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+ OneHotEncoder(handle_unknown='ignore',
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+ sparse=False),
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+ <sklearn.compose._column_transformer.make_column_selector object at 0x000001E750BBC6A0>)])), ('gradientboostingregressor', GradientBoostingRegressor(random_state=42))] |
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+ | verbose | False |
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+ | columntransformer | ColumnTransformer(remainder='passthrough',
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+ transformers=[('onehotencoder',
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+ OneHotEncoder(handle_unknown='ignore',
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+ sparse=False),
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+ <sklearn.compose._column_transformer.make_column_selector object at 0x000001E750BBC6A0>)]) |
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+ | gradientboostingregressor | GradientBoostingRegressor(random_state=42) |
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+ | columntransformer__n_jobs | |
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+ | columntransformer__remainder | passthrough |
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+ | columntransformer__sparse_threshold | 0.3 |
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+ | columntransformer__transformer_weights | |
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+ | columntransformer__transformers | [('onehotencoder', OneHotEncoder(handle_unknown='ignore', sparse=False), <sklearn.compose._column_transformer.make_column_selector object at 0x000001E750BBC6A0>)] |
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+ | columntransformer__verbose | False |
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+ | columntransformer__verbose_feature_names_out | True |
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+ | columntransformer__onehotencoder | OneHotEncoder(handle_unknown='ignore', sparse=False) |
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+ | columntransformer__onehotencoder__categories | auto |
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+ | columntransformer__onehotencoder__drop | |
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+ | columntransformer__onehotencoder__dtype | <class 'numpy.float64'> |
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+ | columntransformer__onehotencoder__handle_unknown | ignore |
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+ | columntransformer__onehotencoder__sparse | False |
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+ | gradientboostingregressor__alpha | 0.9 |
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+ | gradientboostingregressor__ccp_alpha | 0.0 |
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+ | gradientboostingregressor__criterion | friedman_mse |
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+ | gradientboostingregressor__init | |
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+ | gradientboostingregressor__learning_rate | 0.1 |
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+ | gradientboostingregressor__loss | squared_error |
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+ | gradientboostingregressor__max_depth | 3 |
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+ | gradientboostingregressor__max_features | |
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+ | gradientboostingregressor__max_leaf_nodes | |
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+ | gradientboostingregressor__min_impurity_decrease | 0.0 |
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+ | gradientboostingregressor__min_samples_leaf | 1 |
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+ | gradientboostingregressor__min_samples_split | 2 |
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+ | gradientboostingregressor__min_weight_fraction_leaf | 0.0 |
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+ | gradientboostingregressor__n_estimators | 100 |
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+ | gradientboostingregressor__n_iter_no_change | |
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+ | gradientboostingregressor__random_state | 42 |
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+ | gradientboostingregressor__subsample | 1.0 |
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+ | gradientboostingregressor__tol | 0.0001 |
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+ | gradientboostingregressor__validation_fraction | 0.1 |
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+ | gradientboostingregressor__verbose | 0 |
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+ | gradientboostingregressor__warm_start | False |
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+
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+ </details>
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+
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+ ### Model Plot
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+
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+ The model plot is below.
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+
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+ <style>#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 {color: black;background-color: white;}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 pre{padding: 0;}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 div.sk-toggleable {background-color: white;}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 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-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 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-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 div.sk-estimator:hover {background-color: #d4ebff;}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 div.sk-item {z-index: 1;}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 div.sk-parallel::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 div.sk-parallel-item:only-child::after {width: 0;}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 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-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 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-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 div.sk-text-repr-fallback {display: none;}</style><div id="sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,ColumnTransformer(remainder=&#x27;passthrough&#x27;,transformers=[(&#x27;onehotencoder&#x27;,OneHotEncoder(handle_unknown=&#x27;ignore&#x27;,sparse=False),&lt;sklearn.compose._column_transformer.make_column_selector object at 0x000001E750BBC6A0&gt;)])),(&#x27;gradientboostingregressor&#x27;,GradientBoostingRegressor(random_state=42))])</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="f6612892-c085-4dd9-8dca-9cb8081c3777" type="checkbox" ><label for="f6612892-c085-4dd9-8dca-9cb8081c3777" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,ColumnTransformer(remainder=&#x27;passthrough&#x27;,transformers=[(&#x27;onehotencoder&#x27;,OneHotEncoder(handle_unknown=&#x27;ignore&#x27;,sparse=False),&lt;sklearn.compose._column_transformer.make_column_selector object at 0x000001E750BBC6A0&gt;)])),(&#x27;gradientboostingregressor&#x27;,GradientBoostingRegressor(random_state=42))])</pre></div></div></div><div class="sk-serial"><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="3d74f98b-ae31-452d-af87-2c65b0323ba2" type="checkbox" ><label for="3d74f98b-ae31-452d-af87-2c65b0323ba2" class="sk-toggleable__label sk-toggleable__label-arrow">columntransformer: ColumnTransformer</label><div class="sk-toggleable__content"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,transformers=[(&#x27;onehotencoder&#x27;,OneHotEncoder(handle_unknown=&#x27;ignore&#x27;,sparse=False),&lt;sklearn.compose._column_transformer.make_column_selector object at 0x000001E750BBC6A0&gt;)])</pre></div></div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="4af39992-03cf-4522-a288-2db0a787a63c" type="checkbox" ><label for="4af39992-03cf-4522-a288-2db0a787a63c" class="sk-toggleable__label sk-toggleable__label-arrow">onehotencoder</label><div class="sk-toggleable__content"><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x000001E750BBC6A0&gt;</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="519d5e51-5fa6-45d6-a3f7-59c11370402d" type="checkbox" ><label for="519d5e51-5fa6-45d6-a3f7-59c11370402d" class="sk-toggleable__label sk-toggleable__label-arrow">OneHotEncoder</label><div class="sk-toggleable__content"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;, sparse=False)</pre></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="7ede29a7-2614-4eed-a021-e85f1aaa5659" type="checkbox" ><label for="7ede29a7-2614-4eed-a021-e85f1aaa5659" class="sk-toggleable__label sk-toggleable__label-arrow">remainder</label><div class="sk-toggleable__content"><pre>[&#x27;Length1&#x27;, &#x27;Length2&#x27;, &#x27;Length3&#x27;, &#x27;Height&#x27;, &#x27;Width&#x27;]</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="69357535-0314-4987-a311-112335d2cb52" type="checkbox" ><label for="69357535-0314-4987-a311-112335d2cb52" class="sk-toggleable__label sk-toggleable__label-arrow">passthrough</label><div class="sk-toggleable__content"><pre>passthrough</pre></div></div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="f247fbf2-2247-4e99-aaa2-f6fb89ce1b13" type="checkbox" ><label for="f247fbf2-2247-4e99-aaa2-f6fb89ce1b13" class="sk-toggleable__label sk-toggleable__label-arrow">GradientBoostingRegressor</label><div class="sk-toggleable__content"><pre>GradientBoostingRegressor(random_state=42)</pre></div></div></div></div></div></div></div>
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+
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+ ## Evaluation Results
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+
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+ You can find the details about evaluation process and the evaluation results.
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+
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+
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+
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+ | Metric | Value |
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+ |----------|---------|
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+
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+ # How to Get Started with the Model
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+
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+ Use the code below to get started with the model.
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+
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+ <details>
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+ <summary> Click to expand </summary>
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+
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+ ```python
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+ [More Information Needed]
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+ ```
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+
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+ </details>
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+
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+
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+
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+
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+ # Model Card Authors
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+
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+ This model card is written by following authors:
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+
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+ Brenden Connors
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+
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+ # Model Card Contact
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+
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+ You can contact the model card authors through following channels:
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+ [More Information Needed]
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+
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+ # Citation
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+
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+ Below you can find information related to citation.
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+
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+ **BibTeX:**
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+ ```
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+ [More Information Needed]
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+ ```
config.json ADDED
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+ {
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+ "sklearn": {
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+ "columns": [
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+ "Species",
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+ "Length1",
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+ "Length2",
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+ "Length3",
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+ "Height",
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+ "Width"
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+ ],
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+ "environment": [
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+ "scikit-learn=1.0.2"
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+ ],
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+ "example_input": {
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+ "Height": [
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+ 11.52,
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+ 12.48,
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+ 12.3778
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+ ],
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+ "Length1": [
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+ 23.2,
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+ 24.0,
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+ 23.9
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+ ],
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+ "Length2": [
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+ 25.4,
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+ 26.3,
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+ 26.5
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+ ],
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+ "Length3": [
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+ 30.0,
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+ 31.2,
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+ 31.1
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+ ],
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+ "Species": [
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+ "Bream",
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+ "Bream",
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+ "Bream"
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+ ],
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+ "Width": [
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+ 4.02,
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+ 4.3056,
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+ 4.6961
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+ ]
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+ },
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+ "model": {
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+ "file": "example.pkl"
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+ },
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+ "task": "tabular-regression"
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+ }
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+ }
example.pkl ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:10a9f41e1edb4d43ae237933edd469c930e9a1c1635d7ed2dd2743a88c807db2
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+ size 117910