metadata
library_name: quantile-forest
license: apache-2.0
tags:
- quantile-forest
- sklearn
- skops
- tabular-regression
- quantile-regression
- uncertainty-estimation
- prediction-intervals
model_format: pickle
model_file: model.pkl
widget:
- structuredData:
AveBedrms:
- 1.0238095238095237
- 0.9718804920913884
- 1.073446327683616
AveOccup:
- 2.5555555555555554
- 2.109841827768014
- 2.8022598870056497
AveRooms:
- 6.984126984126984
- 6.238137082601054
- 8.288135593220339
HouseAge:
- 41
- 21
- 52
Latitude:
- 37.88
- 37.86
- 37.85
Longitude:
- -122.23
- -122.22
- -122.24
MedInc:
- 8.3252
- 8.3014
- 7.2574
Population:
- 322
- 2401
- 496
Model description
This is a RandomForestQuantileRegressor trained on the California Housing dataset.
Intended uses & limitations
This model is not ready to be used in production.
Training Procedure
The model was trained using default parameters on a 5-fold cross-validation pipeline.
Hyperparameters
Click to expand
Hyperparameter | Value |
---|---|
bootstrap | True |
ccp_alpha | 0.0 |
criterion | squared_error |
default_quantiles | 0.5 |
max_depth | |
max_features | 1.0 |
max_leaf_nodes | |
max_samples | |
max_samples_leaf | 1 |
min_impurity_decrease | 0.0 |
min_samples_leaf | 1 |
min_samples_split | 2 |
min_weight_fraction_leaf | 0.0 |
monotonic_cst | |
n_estimators | 100 |
n_jobs | |
oob_score | False |
random_state | RandomState(MT19937) |
verbose | 0 |
warm_start | False |
Model Plot
RandomForestQuantileRegressor(random_state=RandomState(MT19937) at 0x129E7B440)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
RandomForestQuantileRegressor(random_state=RandomState(MT19937) at 0x129E7B440)
Evaluation Results
Metric | Value |
---|---|
Mean Absolute Percentage Error | 0.164007 |
Median Absolute Error | 0.171 |
Mean Squared Error | 0.25832 |
R-Squared | 0.806 |
How to Get Started with the Model
Click to expand
from examples.plot_qrf_huggingface_inference import CrossValidationPipeline
pipeline = CrossValidationPipeline.load(qrf_pkl_filename)
Model Card Authors
This model card is written by following authors:
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Citation
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BibTeX:
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