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

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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

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from examples.plot_qrf_huggingface_inference import CrossValidationPipeline
pipeline = CrossValidationPipeline.load(qrf_pkl_filename)

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