Commit
•
d5cd935
1
Parent(s):
6299d25
Upload folder using huggingface_hub
Browse files- README.md +17 -87
- model.skops +1 -1
README.md
CHANGED
|
@@ -44,17 +44,11 @@ widget:
|
|
| 44 |
|
| 45 |
# Model description
|
| 46 |
|
| 47 |
-
|
| 48 |
-
|
| 49 |
-
The model is a gradient boosting regressor from sklearn. On top of the standard
|
| 50 |
-
features, it contains predictions from a KNN models. These predictions are calculated
|
| 51 |
-
out of fold, then added on top of the existing features. These features are really
|
| 52 |
-
helpful for decision tree-based models, since those cannot easily learn from geospatial
|
| 53 |
-
data.
|
| 54 |
|
| 55 |
## Intended uses & limitations
|
| 56 |
|
| 57 |
-
|
| 58 |
|
| 59 |
## Training Procedure
|
| 60 |
|
|
@@ -121,96 +115,32 @@ This model is meant for demonstration purposes
|
|
| 121 |
|
| 122 |
### Model Plot
|
| 123 |
|
| 124 |
-
<style>#sk-container-id-
|
| 125 |
|
| 126 |
## Evaluation Results
|
| 127 |
|
| 128 |
-
|
| 129 |
-
|
| 130 |
-
| Metric | Value |
|
| 131 |
-
|-------------------------|--------------|
|
| 132 |
-
| Root mean squared error | 44273.5 |
|
| 133 |
-
| Mean absolute error | 30079.9 |
|
| 134 |
-
| R² | 0.805954 |
|
| 135 |
-
|
| 136 |
-
## Dataset description
|
| 137 |
-
|
| 138 |
-
California Housing dataset
|
| 139 |
-
--------------------------
|
| 140 |
-
|
| 141 |
-
**Data Set Characteristics:**
|
| 142 |
-
|
| 143 |
-
:Number of Instances: 20640
|
| 144 |
-
|
| 145 |
-
:Number of Attributes: 8 numeric, predictive attributes and the target
|
| 146 |
-
|
| 147 |
-
:Attribute Information:
|
| 148 |
-
- MedInc median income in block group
|
| 149 |
-
- HouseAge median house age in block group
|
| 150 |
-
- AveRooms average number of rooms per household
|
| 151 |
-
- AveBedrms average number of bedrooms per household
|
| 152 |
-
- Population block group population
|
| 153 |
-
- AveOccup average number of household members
|
| 154 |
-
- Latitude block group latitude
|
| 155 |
-
- Longitude block group longitude
|
| 156 |
-
|
| 157 |
-
:Missing Attribute Values: None
|
| 158 |
-
|
| 159 |
-
This dataset was obtained from the StatLib repository.
|
| 160 |
-
https://www.dcc.fc.up.pt/~ltorgo/Regression/cal_housing.html
|
| 161 |
-
|
| 162 |
-
The target variable is the median house value for California districts,
|
| 163 |
-
expressed in hundreds of thousands of dollars ($100,000).
|
| 164 |
-
|
| 165 |
-
This dataset was derived from the 1990 U.S. census, using one row per census
|
| 166 |
-
block group. A block group is the smallest geographical unit for which the U.S.
|
| 167 |
-
Census Bureau publishes sample data (a block group typically has a population
|
| 168 |
-
of 600 to 3,000 people).
|
| 169 |
-
|
| 170 |
-
A household is a group of people residing within a home. Since the average
|
| 171 |
-
number of rooms and bedrooms in this dataset are provided per household, these
|
| 172 |
-
columns may take surprisingly large values for block groups with few households
|
| 173 |
-
and many empty houses, such as vacation resorts.
|
| 174 |
-
|
| 175 |
-
It can be downloaded/loaded using the
|
| 176 |
-
:func:`sklearn.datasets.fetch_california_housing` function.
|
| 177 |
-
|
| 178 |
-
.. topic:: References
|
| 179 |
-
|
| 180 |
-
- Pace, R. Kelley and Ronald Barry, Sparse Spatial Autoregressions,
|
| 181 |
-
Statistics and Probability Letters, 33 (1997) 291-297
|
| 182 |
-
|
| 183 |
-
### Data distribution
|
| 184 |
-
|
| 185 |
-
<details>
|
| 186 |
-
<summary> Click to expand </summary>
|
| 187 |
-
|
| 188 |
-
|
| 189 |
-
|
| 190 |
-
</details>
|
| 191 |
|
| 192 |
# How to Get Started with the Model
|
| 193 |
|
| 194 |
-
|
| 195 |
-
|
| 196 |
-
```python
|
| 197 |
-
import json
|
| 198 |
-
import pandas as pd
|
| 199 |
-
import skops.io as sio
|
| 200 |
-
model = sio.load("model.skops")
|
| 201 |
-
with open("config.json") as f:
|
| 202 |
-
config = json.load(f)
|
| 203 |
-
model.predict(pd.DataFrame.from_dict(config["sklearn"]["example_input"]))
|
| 204 |
-
```
|
| 205 |
|
| 206 |
# Model Card Authors
|
| 207 |
|
| 208 |
-
|
|
|
|
|
|
|
| 209 |
|
| 210 |
# Model Card Contact
|
| 211 |
|
| 212 |
-
|
|
|
|
| 213 |
|
| 214 |
-
#
|
| 215 |
|
| 216 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 44 |
|
| 45 |
# Model description
|
| 46 |
|
| 47 |
+
[More Information Needed]
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 48 |
|
| 49 |
## Intended uses & limitations
|
| 50 |
|
| 51 |
+
[More Information Needed]
|
| 52 |
|
| 53 |
## Training Procedure
|
| 54 |
|
|
|
|
| 115 |
|
| 116 |
### Model Plot
|
| 117 |
|
| 118 |
+
<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 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-2 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 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-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 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-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 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-2 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-2" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>StackingRegressor(estimators=[('knn@5',Pipeline(steps=[('select_cols',ColumnTransformer(transformers=[('long_and_lat','passthrough',['Longitude','Latitude'])])),('knn',KNeighborsRegressor())]))],final_estimator=GradientBoostingRegressor(n_estimators=500,random_state=0),passthrough=True)</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-7" type="checkbox" ><label for="sk-estimator-id-7" class="sk-toggleable__label sk-toggleable__label-arrow">StackingRegressor</label><div class="sk-toggleable__content"><pre>StackingRegressor(estimators=[('knn@5',Pipeline(steps=[('select_cols',ColumnTransformer(transformers=[('long_and_lat','passthrough',['Longitude','Latitude'])])),('knn',KNeighborsRegressor())]))],final_estimator=GradientBoostingRegressor(n_estimators=500,random_state=0),passthrough=True)</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><label>knn@5</label></div></div><div class="sk-serial"><div class="sk-item"><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-8" type="checkbox" ><label for="sk-estimator-id-8" class="sk-toggleable__label sk-toggleable__label-arrow">select_cols: ColumnTransformer</label><div class="sk-toggleable__content"><pre>ColumnTransformer(transformers=[('long_and_lat', 'passthrough',['Longitude', 'Latitude'])])</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-9" type="checkbox" ><label for="sk-estimator-id-9" class="sk-toggleable__label sk-toggleable__label-arrow">long_and_lat</label><div class="sk-toggleable__content"><pre>['Longitude', 'Latitude']</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-10" type="checkbox" ><label for="sk-estimator-id-10" 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="sk-estimator-id-11" type="checkbox" ><label for="sk-estimator-id-11" class="sk-toggleable__label sk-toggleable__label-arrow">KNeighborsRegressor</label><div class="sk-toggleable__content"><pre>KNeighborsRegressor()</pre></div></div></div></div></div></div></div></div></div></div><div class="sk-item"><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><label>final_estimator</label></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-12" type="checkbox" ><label for="sk-estimator-id-12" class="sk-toggleable__label sk-toggleable__label-arrow">GradientBoostingRegressor</label><div class="sk-toggleable__content"><pre>GradientBoostingRegressor(n_estimators=500, random_state=0)</pre></div></div></div></div></div></div></div></div></div></div></div></div>
|
| 119 |
|
| 120 |
## Evaluation Results
|
| 121 |
|
| 122 |
+
[More Information Needed]
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 123 |
|
| 124 |
# How to Get Started with the Model
|
| 125 |
|
| 126 |
+
[More Information Needed]
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 127 |
|
| 128 |
# Model Card Authors
|
| 129 |
|
| 130 |
+
This model card is written by following authors:
|
| 131 |
+
|
| 132 |
+
[More Information Needed]
|
| 133 |
|
| 134 |
# Model Card Contact
|
| 135 |
|
| 136 |
+
You can contact the model card authors through following channels:
|
| 137 |
+
[More Information Needed]
|
| 138 |
|
| 139 |
+
# Citation
|
| 140 |
|
| 141 |
+
Below you can find information related to citation.
|
| 142 |
+
|
| 143 |
+
**BibTeX:**
|
| 144 |
+
```
|
| 145 |
+
[More Information Needed]
|
| 146 |
+
```
|
model.skops
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 14969690
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:e4a226e7d8e8510ae93efd6716ec4d07674e7578748396ae697f98aa4bd2bc09
|
| 3 |
size 14969690
|