Label Encoding for Regression Networks

Shah, Deval; Xue, Zi Yu; Aamodt, Tor M.

doi:10.48550/arxiv.2212.01927

Cited by 2 publications

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“…Categorical variables were presented as numerical counts alongside their corresponding proportions and labeled in the form of One-Hot encoding 14 or label encoding. 15 On the other hand, for those continuous variables that did not conform to a normal distribution, a log-transformation was applied. The specific details regarding the labeling process during training are presented in the Supplementary Tables 1-1 and 1-2.…”

Section: Discussionmentioning

confidence: 99%

LightGBM outperforms other machine learning techniques in predicting graft failure after liver transplantation: Creation of a predictive model through large‐scale analysis

Yanagawa,

Iwadoh,

Akabane

et al. 2024

Clinical Transplantation

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BackgroundThe incidence of graft failure following liver transplantation (LTx) is consistent. While traditional risk scores for LTx have limited accuracy, the potential of machine learning (ML) in this area remains uncertain, despite its promise in other transplant domains. This study aims to determine ML's predictive limitations in LTx by replicating methods used in previous heart transplant research.MethodsThis study utilized the UNOS STAR database, selecting 64,384 adult patients who underwent LTx between 2010 and 2020. Gradient boosting models (XGBoost and LightGBM) were used to predict 14, 30, and 90‐day graft failure compared to conventional logistic regression model. Models were evaluated using both shuffled and rolling cross‐validation (CV) methodologies. Model performance was assessed using the AUC across validation iterations.ResultsIn a study comparing predictive models for 14‐day, 30‐day and 90‐day graft survival, LightGBM consistently outperformed other models, achieving the highest AUC of.740,.722, and.700 in shuffled CV methods. However, in rolling CV the accuracy of the model declined across every ML algorithm. The analysis revealed influential factors for graft survival prediction across all models, including total bilirubin, medical condition, recipient age, and donor AST, among others. Several features like donor age and recipient diabetes history were important in two out of three models.ConclusionsLightGBM enhances short‐term graft survival predictions post‐LTx. However, due to changing medical practices and selection criteria, continuous model evaluation is essential. Future studies should focus on temporal variations, clinical implications, and ensure model transparency for broader medical utility.

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Section: Discussionmentioning

confidence: 99%

LightGBM outperforms other machine learning techniques in predicting graft failure after liver transplantation: Creation of a predictive model through large‐scale analysis

Yanagawa,

Iwadoh,

Akabane

et al. 2024

Clinical Transplantation

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“…Using the monthly variable, seasonal categorical data were created (autumn, winter, spring, or summer) based on the corresponding month. Before modeling, the seasonal data were converted into numeric form using label encoding [37]. For this case, the data were converted into a number sequence: {0,1,2,3}, where 0 represents autumn, 1 represents winter, 2 represents spring, and 3 represents summer.…”

Section: Feature Engineeringmentioning

confidence: 99%

Data-Driven Modeling of Appliance Energy Usage

Assadian,

Assadian

2023

Energies

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Due to the transition toward the Internet of Everything (IOE), the prediction of energy consumed by household appliances has become a progressively more difficult topic to model. Even with advancements in data analytics and machine learning, several challenges remain to be addressed. Therefore, providing highly accurate and optimized models has become the primary research goal of many studies. This paper analyzes appliance energy consumption through a variety of machine learning-based strategies. Utilizing data recorded from a single-family home, input variables comprised internal temperatures and humidities, lighting consumption, and outdoor conditions including wind speed, visibility, and pressure. Various models were trained and evaluated: (a) multiple linear regression, (b) support vector regression, (c) random forest, (d) gradient boosting, (e) xgboost, and (f) the extra trees regressor. Both feature engineering and hyperparameter tuning methodologies were applied to not only extend existing features but also create new ones that provided improved model performance across all metrics: root mean square error (RMSE), coefficient of determination (R2), mean absolute error (MAE), and mean absolute percentage error (MAPE). The best model (extra trees) was able to explain 99% of the variance in the training set and 66% in the testing set when using all the predictors. The results were compared with those obtained using a similar methodology. The objective of performing these actions was to show a unique perspective in simulating building performance through data-driven models, identifying how to maximize predictive performance through the use of machine learning-based strategies, as well as understanding the potential benefits of utilizing different models.

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Label Encoding for Regression Networks

Cited by 2 publications

References 0 publications

LightGBM outperforms other machine learning techniques in predicting graft failure after liver transplantation: Creation of a predictive model through large‐scale analysis

LightGBM outperforms other machine learning techniques in predicting graft failure after liver transplantation: Creation of a predictive model through large‐scale analysis

Data-Driven Modeling of Appliance Energy Usage

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