lcd-bias-correction

martibosch/lcd-bias-correction

lcd-bias-correction is a tabular regression model from martibosch. Use it for the tabular regression task on the model card, and read the license before you ship it in a product. It is set up for sklearn. The card lists the license as cc-by-4.0.

Downloads · 30 days

Access

Public

Updated Jun 10, 2026

Repo size

3.9 MB

Likes

Public

Hugging Face

Repo makeup

Click a slice to open those files.

.skops784 KB · 95%

At a glance

Task: Tabular Regression
Library: sklearn
License: cc-by-4.0
Access: Public
Created: Jun 1, 2026
Updated: Jun 10, 2026
SHA: 60f6d2a1

Model card

From the Hugging Face model README

Model description

Radiation-based temperature bias correction models for low-cost temperature sensors (LCDs), trained on parallel measurements against the MeteoSwiss Zollikofen (BER) reference station (summer 2025). One Explainable Boosting Machine (EBM) model per sensor type, wrapped in a scikit-learn Pipeline with a BestScaleRadiationTransformer from the meteora library that selects the optimal radiation integration window. Models are serialized with skops.

Intended uses & limitations

These models are intended to correct shortwave radiation-induced temperature bias in low-cost outdoor sensors. They are trained on Swiss summer data and may not generalise to other climates or seasons.

Training Procedure

Each model is fitted on hourly temperature bias (ΔT = T_LCD − T_ref) as a function of accumulated shortwave radiation from the nearest MeteoSwiss AWS. The optimal radiation integration window (1–5 h) is selected per sensor by meteora's BestScaleRadiationTransformer, which maximizes Pearson correlation with ΔT before fitting the EBM.

Hyperparameters

<details> <summary> Click to expand </summary>

Hyperparameter	Value
memory	None
steps	[('radiation_transformer', BestScaleRadiationTransformer(radiation_col='radiation_shortwave',<br /> time_col='time',<br /> window_minutes=[60, 120, 180, 240, 300])), ('model', ExplainableBoostingRegressor())]
transform_input	None
verbose	False
radiation_transformer	BestScaleRadiationTransformer(radiation_col='radiation_shortwave',<br /> time_col='time',<br /> window_minutes=[60, 120, 180, 240, 300])
model	ExplainableBoostingRegressor()
radiation_transformer__radiation_col	radiation_shortwave
radiation_transformer__time_col	time
radiation_transformer__window_minutes	[60, 120, 180, 240, 300]
model__callback	None
model__cat_smooth	10.0
model__cyclic_progress	False
model__early_stopping_rounds	100
model__early_stopping_tolerance	1e-05
model__exclude	None
model__feature_names	None
model__feature_types	None
model__gain_scale	5.0
model__greedy_ratio	10.0
model__inner_bags	0
model__interaction_smoothing_rounds	100
model__interactions	5x
model__learning_rate	0.04
model__max_bins	1024
model__max_delta_step	0.0
model__max_interaction_bins	64
model__max_leaves	2
model__max_rounds	50000
model__min_cat_samples	10
model__min_hessian	0.0
model__min_samples_leaf	4
model__missing	separate
model__monotone_constraints	None
model__n_jobs	-2
model__objective	rmse
model__outer_bags	14
model__random_state	42
model__reg_alpha	0.0
model__reg_lambda	0.0
model__smoothing_rounds	500
model__validation_size	0.15

</details>

Model Plot

}document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {const toggleableContent = element.closest('.sk-toggleable__content');const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';const paramName = element.parentElement.nextElementSibling.textContent.trim();const fullParamName = paramPrefix ? ${paramPrefix}${paramName} : paramName;element.setAttribute('title', fullParamName); }); </script></body>

Evaluation Results

Device	File	Training R²	Best radiation window
Abilium	`abilium.skops`	0.591	60 min
Barani	`barani.skops`	0.100	300 min
Decentlab	`decentlab.skops`	0.755	120 min
Koalasense	`koalasense.skops`	0.362	60 min
Onset_big	`onset-big.skops`	0.215	300 min

How to Get Started with the Model

from huggingface_hub import hf_hub_download
from skops import io as skops_io
from meteora.bias_correction import apply_bias_correction, parse_hf_path

model_str = "martibosch/lcd-bias-correction/decentlab.skops"  # replace with your sensor
repo_id, filename = parse_hf_path(model_str)
trusted = skops_io.get_untrusted_types(
    file=hf_hub_download(repo_id, filename)
)
cor_ts_df = apply_bias_correction(
    lcd_ts_df,
    ref_rad_ts,  # pd.Series/DataFrame/xr.Dataset with shortwave radiation
    model_str,
    trusted=trusted,
)

Model Card Authors

Martí Bosch, Moritz Burger

Model Card Contact

[email protected]

Citation

@misc{bosch2026revisiting,
      title={Revisiting urban heat indices in Switzerland using low-cost measurement networks}, 
      author={Martí Bosch and Moritz Burger},
      year={2026},
      eprint={2606.09364},
      archivePrefix={arXiv},
      primaryClass={physics.ao-ph},
      url={https://arxiv.org/abs/2606.09364}, 
}

Task: Tabular Regression
Library: sklearn
License: cc-by-4.0
Created: Jun 1, 2026
Updated: Jun 10, 2026