--- library_name: sklearn tags: - sklearn - skops - tabular-regression model_format: skops model_file: model.skops widget: structuredData: AveBedrms: - 0.9290780141843972 - 0.9458483754512635 - 1.087360594795539 AveOccup: - 3.1134751773049647 - 3.0613718411552346 - 3.2657992565055762 AveRooms: - 6.304964539007092 - 6.945848375451264 - 3.8884758364312266 HouseAge: - 17.0 - 15.0 - 24.0 Latitude: - 34.23 - 36.84 - 34.04 Longitude: - -117.41 - -119.77 - -118.3 MedInc: - 6.1426 - 5.3886 - 1.7109 Population: - 439.0 - 848.0 - 1757.0 --- # Model description Gradient boosting regressor trained on California Housing dataset The model is a gradient boosting regressor from sklearn. On top of the standard features, it contains predictions from a KNN models. These predictions are calculated out of fold, then added on top of the existing features. These features are really helpful for decision tree-based models, since those cannot easily learn from geospatial data. ## Intended uses & limitations This model is meant for demonstration purposes ## Training Procedure ### Hyperparameters The model is trained with below hyperparameters.
Click to expand | Hyperparameter | Value | |-----------------------------------------------|--------------------------------------------------------------| | cv | | | estimators | [('knn@5', Pipeline(steps=[('select_cols',
ColumnTransformer(transformers=[('long_and_lat', 'passthrough',
['Longitude', 'Latitude'])])),
('knn', KNeighborsRegressor())]))] | | final_estimator__alpha | 0.9 | | final_estimator__ccp_alpha | 0.0 | | final_estimator__criterion | friedman_mse | | final_estimator__init | | | final_estimator__learning_rate | 0.1 | | final_estimator__loss | squared_error | | final_estimator__max_depth | 3 | | final_estimator__max_features | | | final_estimator__max_leaf_nodes | | | final_estimator__min_impurity_decrease | 0.0 | | final_estimator__min_samples_leaf | 1 | | final_estimator__min_samples_split | 2 | | final_estimator__min_weight_fraction_leaf | 0.0 | | final_estimator__n_estimators | 500 | | final_estimator__n_iter_no_change | | | final_estimator__random_state | 0 | | final_estimator__subsample | 1.0 | | final_estimator__tol | 0.0001 | | final_estimator__validation_fraction | 0.1 | | final_estimator__verbose | 0 | | final_estimator__warm_start | False | | final_estimator | GradientBoostingRegressor(n_estimators=500, random_state=0) | | n_jobs | | | passthrough | True | | verbose | 0 | | knn@5 | Pipeline(steps=[('select_cols',
ColumnTransformer(transformers=[('long_and_lat', 'passthrough',
['Longitude', 'Latitude'])])),
('knn', KNeighborsRegressor())]) | | knn@5__memory | | | knn@5__steps | [('select_cols', ColumnTransformer(transformers=[('long_and_lat', 'passthrough',
['Longitude', 'Latitude'])])), ('knn', KNeighborsRegressor())] | | knn@5__verbose | False | | knn@5__select_cols | ColumnTransformer(transformers=[('long_and_lat', 'passthrough',
['Longitude', 'Latitude'])]) | | knn@5__knn | KNeighborsRegressor() | | knn@5__select_cols__n_jobs | | | knn@5__select_cols__remainder | drop | | knn@5__select_cols__sparse_threshold | 0.3 | | knn@5__select_cols__transformer_weights | | | knn@5__select_cols__transformers | [('long_and_lat', 'passthrough', ['Longitude', 'Latitude'])] | | knn@5__select_cols__verbose | False | | knn@5__select_cols__verbose_feature_names_out | True | | knn@5__select_cols__long_and_lat | passthrough | | knn@5__knn__algorithm | auto | | knn@5__knn__leaf_size | 30 | | knn@5__knn__metric | minkowski | | knn@5__knn__metric_params | | | knn@5__knn__n_jobs | | | knn@5__knn__n_neighbors | 5 | | knn@5__knn__p | 2 | | knn@5__knn__weights | uniform |
### Model Plot The model plot is below. 
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)
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## Evaluation Results Metrics are calculated on the test set | Metric | Value | |-------------------------|--------------| | Root mean squared error | 44273.5 | | Mean absolute error | 30079.9 | | R² | 0.805954 | ## Dataset description California Housing dataset -------------------------- **Data Set Characteristics:** :Number of Instances: 20640 :Number of Attributes: 8 numeric, predictive attributes and the target :Attribute Information: - MedInc median income in block group - HouseAge median house age in block group - AveRooms average number of rooms per household - AveBedrms average number of bedrooms per household - Population block group population - AveOccup average number of household members - Latitude block group latitude - Longitude block group longitude :Missing Attribute Values: None This dataset was obtained from the StatLib repository. https://www.dcc.fc.up.pt/~ltorgo/Regression/cal_housing.html The target variable is the median house value for California districts, expressed in hundreds of thousands of dollars ($100,000). This dataset was derived from the 1990 U.S. census, using one row per census block group. A block group is the smallest geographical unit for which the U.S. Census Bureau publishes sample data (a block group typically has a population of 600 to 3,000 people). An household is a group of people residing within a home. Since the average number of rooms and bedrooms in this dataset are provided per household, these columns may take surpinsingly large values for block groups with few households and many empty houses, such as vacation resorts. It can be downloaded/loaded using the :func:`sklearn.datasets.fetch_california_housing` function. .. topic:: References - Pace, R. Kelley and Ronald Barry, Sparse Spatial Autoregressions, Statistics and Probability Letters, 33 (1997) 291-297 ### Data distribution
Click to expand ![Data distribution](geographic.png)
# How to Get Started with the Model Run the code below to load the model ```python import json import pandas as pd import skops.io as sio model = sio.load("model.skops") with open("config.json") as f: config = json.load(f) model.predict(pd.DataFrame.from_dict(config["sklearn"]["example_input"])) ``` # Model Card Authors Benjamin Bossan # Model Card Contact benjamin@huggingface.co # Permutation Importances ![Permutation Importances](permutation-importances.png)