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Browse files- README.md +203 -0
- config.json +137 -0
- evaluation.png +0 -0
- model.pkl +3 -0
README.md
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| 1 |
+
---
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| 2 |
+
license: mit
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| 3 |
+
library_name: sklearn
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| 4 |
+
tags:
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| 5 |
+
- sklearn
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| 6 |
+
- skops
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| 7 |
+
- tabular-classification
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| 8 |
+
model_format: pickle
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| 9 |
+
model_file: model.pkl
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| 10 |
+
widget:
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| 11 |
+
structuredData:
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| 12 |
+
BsmtFinSF1:
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| 13 |
+
- 1280
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| 14 |
+
- 1464
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| 15 |
+
- 0
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| 16 |
+
BsmtUnfSF:
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| 17 |
+
- 402
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| 18 |
+
- 536
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| 19 |
+
- 795
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| 20 |
+
Condition2:
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| 21 |
+
- Norm
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| 22 |
+
- Norm
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| 23 |
+
- Norm
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| 24 |
+
ExterQual:
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| 25 |
+
- Ex
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| 26 |
+
- Gd
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| 27 |
+
- Gd
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| 28 |
+
Foundation:
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| 29 |
+
- PConc
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| 30 |
+
- PConc
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| 31 |
+
- PConc
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| 32 |
+
GarageCars:
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| 33 |
+
- 3
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| 34 |
+
- 3
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| 35 |
+
- 1
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| 36 |
+
GarageType:
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| 37 |
+
- BuiltIn
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| 38 |
+
- Attchd
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| 39 |
+
- Detchd
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| 40 |
+
Heating:
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| 41 |
+
- GasA
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| 42 |
+
- GasA
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| 43 |
+
- GasA
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| 44 |
+
HeatingQC:
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| 45 |
+
- Ex
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| 46 |
+
- Ex
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| 47 |
+
- TA
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| 48 |
+
HouseStyle:
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| 49 |
+
- 2Story
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| 50 |
+
- 1Story
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| 51 |
+
- 2.5Fin
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| 52 |
+
MSSubClass:
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| 53 |
+
- 60
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| 54 |
+
- 20
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| 55 |
+
- 75
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| 56 |
+
MasVnrArea:
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| 57 |
+
- 272.0
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| 58 |
+
- 246.0
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| 59 |
+
- 0.0
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| 60 |
+
MasVnrType:
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| 61 |
+
- Stone
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| 62 |
+
- Stone
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| 63 |
+
- .nan
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| 64 |
+
MiscFeature:
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| 65 |
+
- .nan
|
| 66 |
+
- .nan
|
| 67 |
+
- .nan
|
| 68 |
+
MoSold:
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| 69 |
+
- 8
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| 70 |
+
- 7
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| 71 |
+
- 3
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| 72 |
+
OverallQual:
|
| 73 |
+
- 10
|
| 74 |
+
- 8
|
| 75 |
+
- 4
|
| 76 |
+
Street:
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| 77 |
+
- Pave
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| 78 |
+
- Pave
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| 79 |
+
- Pave
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| 80 |
+
TotalBsmtSF:
|
| 81 |
+
- 1682
|
| 82 |
+
- 2000
|
| 83 |
+
- 795
|
| 84 |
+
YearRemodAdd:
|
| 85 |
+
- 2008
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| 86 |
+
- 2005
|
| 87 |
+
- 1950
|
| 88 |
+
YrSold:
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| 89 |
+
- 2008
|
| 90 |
+
- 2007
|
| 91 |
+
- 2006
|
| 92 |
+
---
|
| 93 |
+
|
| 94 |
+
# Model description
|
| 95 |
+
|
| 96 |
+
This is a gradient boosted regression model trained on ames housing dataset from OpenML.
|
| 97 |
+
|
| 98 |
+
## Intended uses & limitations
|
| 99 |
+
|
| 100 |
+
[More Information Needed]
|
| 101 |
+
|
| 102 |
+
## Training Procedure
|
| 103 |
+
|
| 104 |
+
[More Information Needed]
|
| 105 |
+
|
| 106 |
+
### Hyperparameters
|
| 107 |
+
|
| 108 |
+
<details>
|
| 109 |
+
<summary> Click to expand </summary>
|
| 110 |
+
|
| 111 |
+
| Hyperparameter | Value |
|
| 112 |
+
|----------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
|
| 113 |
+
| memory | |
|
| 114 |
+
| steps | [('columntransformer', ColumnTransformer(transformers=[('simpleimputer',<br /> SimpleImputer(add_indicator=True),<br /> <sklearn.compose._column_transformer.make_column_selector object at 0x000002A2B7A2B730>),<br /> ('ordinalencoder',<br /> OrdinalEncoder(encoded_missing_value=-2,<br /> handle_unknown='use_encoded_value',<br /> unknown_value=-1),<br /> <sklearn.compose._column_transformer.make_column_selector object at 0x000002A2EC9B9180>)])), ('histgradientboostingregressor', HistGradientBoostingRegressor(random_state=0))] |
|
| 115 |
+
| verbose | False |
|
| 116 |
+
| columntransformer | ColumnTransformer(transformers=[('simpleimputer',<br /> SimpleImputer(add_indicator=True),<br /> <sklearn.compose._column_transformer.make_column_selector object at 0x000002A2B7A2B730>),<br /> ('ordinalencoder',<br /> OrdinalEncoder(encoded_missing_value=-2,<br /> handle_unknown='use_encoded_value',<br /> unknown_value=-1),<br /> <sklearn.compose._column_transformer.make_column_selector object at 0x000002A2EC9B9180>)]) |
|
| 117 |
+
| histgradientboostingregressor | HistGradientBoostingRegressor(random_state=0) |
|
| 118 |
+
| columntransformer__n_jobs | |
|
| 119 |
+
| columntransformer__remainder | drop |
|
| 120 |
+
| columntransformer__sparse_threshold | 0.3 |
|
| 121 |
+
| columntransformer__transformer_weights | |
|
| 122 |
+
| columntransformer__transformers | [('simpleimputer', SimpleImputer(add_indicator=True), <sklearn.compose._column_transformer.make_column_selector object at 0x000002A2B7A2B730>), ('ordinalencoder', OrdinalEncoder(encoded_missing_value=-2, handle_unknown='use_encoded_value',<br /> unknown_value=-1), <sklearn.compose._column_transformer.make_column_selector object at 0x000002A2EC9B9180>)] |
|
| 123 |
+
| columntransformer__verbose | False |
|
| 124 |
+
| columntransformer__verbose_feature_names_out | True |
|
| 125 |
+
| columntransformer__simpleimputer | SimpleImputer(add_indicator=True) |
|
| 126 |
+
| columntransformer__ordinalencoder | OrdinalEncoder(encoded_missing_value=-2, handle_unknown='use_encoded_value',<br /> unknown_value=-1) |
|
| 127 |
+
| columntransformer__simpleimputer__add_indicator | True |
|
| 128 |
+
| columntransformer__simpleimputer__copy | True |
|
| 129 |
+
| columntransformer__simpleimputer__fill_value | |
|
| 130 |
+
| columntransformer__simpleimputer__keep_empty_features | False |
|
| 131 |
+
| columntransformer__simpleimputer__missing_values | nan |
|
| 132 |
+
| columntransformer__simpleimputer__strategy | mean |
|
| 133 |
+
| columntransformer__simpleimputer__verbose | deprecated |
|
| 134 |
+
| columntransformer__ordinalencoder__categories | auto |
|
| 135 |
+
| columntransformer__ordinalencoder__dtype | <class 'numpy.float64'> |
|
| 136 |
+
| columntransformer__ordinalencoder__encoded_missing_value | -2 |
|
| 137 |
+
| columntransformer__ordinalencoder__handle_unknown | use_encoded_value |
|
| 138 |
+
| columntransformer__ordinalencoder__unknown_value | -1 |
|
| 139 |
+
| histgradientboostingregressor__categorical_features | |
|
| 140 |
+
| histgradientboostingregressor__early_stopping | auto |
|
| 141 |
+
| histgradientboostingregressor__interaction_cst | |
|
| 142 |
+
| histgradientboostingregressor__l2_regularization | 0.0 |
|
| 143 |
+
| histgradientboostingregressor__learning_rate | 0.1 |
|
| 144 |
+
| histgradientboostingregressor__loss | squared_error |
|
| 145 |
+
| histgradientboostingregressor__max_bins | 255 |
|
| 146 |
+
| histgradientboostingregressor__max_depth | |
|
| 147 |
+
| histgradientboostingregressor__max_iter | 100 |
|
| 148 |
+
| histgradientboostingregressor__max_leaf_nodes | 31 |
|
| 149 |
+
| histgradientboostingregressor__min_samples_leaf | 20 |
|
| 150 |
+
| histgradientboostingregressor__monotonic_cst | |
|
| 151 |
+
| histgradientboostingregressor__n_iter_no_change | 10 |
|
| 152 |
+
| histgradientboostingregressor__quantile | |
|
| 153 |
+
| histgradientboostingregressor__random_state | 0 |
|
| 154 |
+
| histgradientboostingregressor__scoring | loss |
|
| 155 |
+
| histgradientboostingregressor__tol | 1e-07 |
|
| 156 |
+
| histgradientboostingregressor__validation_fraction | 0.1 |
|
| 157 |
+
| histgradientboostingregressor__verbose | 0 |
|
| 158 |
+
| histgradientboostingregressor__warm_start | False |
|
| 159 |
+
|
| 160 |
+
</details>
|
| 161 |
+
|
| 162 |
+
### Model Plot
|
| 163 |
+
|
| 164 |
+
<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-container-id-1 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-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 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;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 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-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-1" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[('columntransformer',ColumnTransformer(transformers=[('simpleimputer',SimpleImputer(add_indicator=True),<sklearn.compose._column_transformer.make_column_selector object at 0x000002A2B7A2B730>),('ordinalencoder',OrdinalEncoder(encoded_missing_value=-2,handle_unknown='use_encoded_value',unknown_value=-1),<sklearn.compose._column_transformer.make_column_selector object at 0x000002A2EC9B9180>)])),('histgradientboostingregressor',HistGradientBoostingRegressor(random_state=0))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</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="sk-estimator-id-1" type="checkbox" ><label for="sk-estimator-id-1" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[('columntransformer',ColumnTransformer(transformers=[('simpleimputer',SimpleImputer(add_indicator=True),<sklearn.compose._column_transformer.make_column_selector object at 0x000002A2B7A2B730>),('ordinalencoder',OrdinalEncoder(encoded_missing_value=-2,handle_unknown='use_encoded_value',unknown_value=-1),<sklearn.compose._column_transformer.make_column_selector object at 0x000002A2EC9B9180>)])),('histgradientboostingregressor',HistGradientBoostingRegressor(random_state=0))])</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="sk-estimator-id-2" type="checkbox" ><label for="sk-estimator-id-2" class="sk-toggleable__label sk-toggleable__label-arrow">columntransformer: ColumnTransformer</label><div class="sk-toggleable__content"><pre>ColumnTransformer(transformers=[('simpleimputer',SimpleImputer(add_indicator=True),<sklearn.compose._column_transformer.make_column_selector object at 0x000002A2B7A2B730>),('ordinalencoder',OrdinalEncoder(encoded_missing_value=-2,handle_unknown='use_encoded_value',unknown_value=-1),<sklearn.compose._column_transformer.make_column_selector object at 0x000002A2EC9B9180>)])</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="sk-estimator-id-3" type="checkbox" ><label for="sk-estimator-id-3" class="sk-toggleable__label sk-toggleable__label-arrow">simpleimputer</label><div class="sk-toggleable__content"><pre><sklearn.compose._column_transformer.make_column_selector object at 0x000002A2B7A2B730></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="sk-estimator-id-4" type="checkbox" ><label for="sk-estimator-id-4" class="sk-toggleable__label sk-toggleable__label-arrow">SimpleImputer</label><div class="sk-toggleable__content"><pre>SimpleImputer(add_indicator=True)</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="sk-estimator-id-5" type="checkbox" ><label for="sk-estimator-id-5" class="sk-toggleable__label sk-toggleable__label-arrow">ordinalencoder</label><div class="sk-toggleable__content"><pre><sklearn.compose._column_transformer.make_column_selector object at 0x000002A2EC9B9180></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="sk-estimator-id-6" type="checkbox" ><label for="sk-estimator-id-6" class="sk-toggleable__label sk-toggleable__label-arrow">OrdinalEncoder</label><div class="sk-toggleable__content"><pre>OrdinalEncoder(encoded_missing_value=-2, handle_unknown='use_encoded_value',unknown_value=-1)</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="sk-estimator-id-7" type="checkbox" ><label for="sk-estimator-id-7" class="sk-toggleable__label sk-toggleable__label-arrow">HistGradientBoostingRegressor</label><div class="sk-toggleable__content"><pre>HistGradientBoostingRegressor(random_state=0)</pre></div></div></div></div></div></div></div>
|
| 165 |
+
|
| 166 |
+
## Evaluation Results
|
| 167 |
+
|
| 168 |
+
| Metric | Value |
|
| 169 |
+
|----------|----------|
|
| 170 |
+
| R2 score | 0.838471 |
|
| 171 |
+
| MAE | 0.111495 |
|
| 172 |
+
|
| 173 |
+
# How to Get Started with the Model
|
| 174 |
+
|
| 175 |
+
[More Information Needed]
|
| 176 |
+
|
| 177 |
+
# Model Card Authors
|
| 178 |
+
|
| 179 |
+
This model card is written by following authors:
|
| 180 |
+
|
| 181 |
+
[More Information Needed]
|
| 182 |
+
|
| 183 |
+
# Model Card Contact
|
| 184 |
+
|
| 185 |
+
You can contact the model card authors through following channels:
|
| 186 |
+
[More Information Needed]
|
| 187 |
+
|
| 188 |
+
# Citation
|
| 189 |
+
|
| 190 |
+
Below you can find information related to citation.
|
| 191 |
+
|
| 192 |
+
**BibTeX:**
|
| 193 |
+
```
|
| 194 |
+
[More Information Needed]
|
| 195 |
+
```
|
| 196 |
+
|
| 197 |
+
# Intended uses & limitations
|
| 198 |
+
|
| 199 |
+
This model is not ready to be used in production.
|
| 200 |
+
|
| 201 |
+
# Evaluation
|
| 202 |
+
|
| 203 |
+
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config.json
ADDED
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@@ -0,0 +1,137 @@
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|
| 1 |
+
{
|
| 2 |
+
"sklearn": {
|
| 3 |
+
"columns": [
|
| 4 |
+
"YrSold",
|
| 5 |
+
"HeatingQC",
|
| 6 |
+
"Street",
|
| 7 |
+
"YearRemodAdd",
|
| 8 |
+
"Heating",
|
| 9 |
+
"MasVnrType",
|
| 10 |
+
"BsmtUnfSF",
|
| 11 |
+
"Foundation",
|
| 12 |
+
"MasVnrArea",
|
| 13 |
+
"MSSubClass",
|
| 14 |
+
"ExterQual",
|
| 15 |
+
"Condition2",
|
| 16 |
+
"GarageCars",
|
| 17 |
+
"GarageType",
|
| 18 |
+
"OverallQual",
|
| 19 |
+
"TotalBsmtSF",
|
| 20 |
+
"BsmtFinSF1",
|
| 21 |
+
"HouseStyle",
|
| 22 |
+
"MiscFeature",
|
| 23 |
+
"MoSold"
|
| 24 |
+
],
|
| 25 |
+
"environment": [
|
| 26 |
+
"scikit-learn=1.2.2"
|
| 27 |
+
],
|
| 28 |
+
"example_input": {
|
| 29 |
+
"BsmtFinSF1": [
|
| 30 |
+
1280,
|
| 31 |
+
1464,
|
| 32 |
+
0
|
| 33 |
+
],
|
| 34 |
+
"BsmtUnfSF": [
|
| 35 |
+
402,
|
| 36 |
+
536,
|
| 37 |
+
795
|
| 38 |
+
],
|
| 39 |
+
"Condition2": [
|
| 40 |
+
"Norm",
|
| 41 |
+
"Norm",
|
| 42 |
+
"Norm"
|
| 43 |
+
],
|
| 44 |
+
"ExterQual": [
|
| 45 |
+
"Ex",
|
| 46 |
+
"Gd",
|
| 47 |
+
"Gd"
|
| 48 |
+
],
|
| 49 |
+
"Foundation": [
|
| 50 |
+
"PConc",
|
| 51 |
+
"PConc",
|
| 52 |
+
"PConc"
|
| 53 |
+
],
|
| 54 |
+
"GarageCars": [
|
| 55 |
+
3,
|
| 56 |
+
3,
|
| 57 |
+
1
|
| 58 |
+
],
|
| 59 |
+
"GarageType": [
|
| 60 |
+
"BuiltIn",
|
| 61 |
+
"Attchd",
|
| 62 |
+
"Detchd"
|
| 63 |
+
],
|
| 64 |
+
"Heating": [
|
| 65 |
+
"GasA",
|
| 66 |
+
"GasA",
|
| 67 |
+
"GasA"
|
| 68 |
+
],
|
| 69 |
+
"HeatingQC": [
|
| 70 |
+
"Ex",
|
| 71 |
+
"Ex",
|
| 72 |
+
"TA"
|
| 73 |
+
],
|
| 74 |
+
"HouseStyle": [
|
| 75 |
+
"2Story",
|
| 76 |
+
"1Story",
|
| 77 |
+
"2.5Fin"
|
| 78 |
+
],
|
| 79 |
+
"MSSubClass": [
|
| 80 |
+
60,
|
| 81 |
+
20,
|
| 82 |
+
75
|
| 83 |
+
],
|
| 84 |
+
"MasVnrArea": [
|
| 85 |
+
272.0,
|
| 86 |
+
246.0,
|
| 87 |
+
0.0
|
| 88 |
+
],
|
| 89 |
+
"MasVnrType": [
|
| 90 |
+
"Stone",
|
| 91 |
+
"Stone",
|
| 92 |
+
NaN
|
| 93 |
+
],
|
| 94 |
+
"MiscFeature": [
|
| 95 |
+
NaN,
|
| 96 |
+
NaN,
|
| 97 |
+
NaN
|
| 98 |
+
],
|
| 99 |
+
"MoSold": [
|
| 100 |
+
8,
|
| 101 |
+
7,
|
| 102 |
+
3
|
| 103 |
+
],
|
| 104 |
+
"OverallQual": [
|
| 105 |
+
10,
|
| 106 |
+
8,
|
| 107 |
+
4
|
| 108 |
+
],
|
| 109 |
+
"Street": [
|
| 110 |
+
"Pave",
|
| 111 |
+
"Pave",
|
| 112 |
+
"Pave"
|
| 113 |
+
],
|
| 114 |
+
"TotalBsmtSF": [
|
| 115 |
+
1682,
|
| 116 |
+
2000,
|
| 117 |
+
795
|
| 118 |
+
],
|
| 119 |
+
"YearRemodAdd": [
|
| 120 |
+
2008,
|
| 121 |
+
2005,
|
| 122 |
+
1950
|
| 123 |
+
],
|
| 124 |
+
"YrSold": [
|
| 125 |
+
2008,
|
| 126 |
+
2007,
|
| 127 |
+
2006
|
| 128 |
+
]
|
| 129 |
+
},
|
| 130 |
+
"model": {
|
| 131 |
+
"file": "model.pkl"
|
| 132 |
+
},
|
| 133 |
+
"model_format": "pickle",
|
| 134 |
+
"task": "tabular-classification",
|
| 135 |
+
"use_intelex": false
|
| 136 |
+
}
|
| 137 |
+
}
|
evaluation.png
ADDED
|
model.pkl
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:96f6b665f15c61147b0c5edcf6f97d29db42963524c5333baea2dc38ae208912
|
| 3 |
+
size 346554
|