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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)
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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)

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Citation

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