Then we apply a Lasso-logistic regression learning ensemble to evaluate the credit risks. In this research, the data is first balanced and diversified by clustering and bagging algorithms. In this paper, given large unbalanced data, we consider the plausibility of ensemble learning using regularized logistic regression as the base classifier to deal with credit scoring problems. However, for various reasons, there has been little research using logistic regression as the base classifier. Recently, various ensemble learning methods with different base classifiers have been proposed for credit scoring problems. Large unbalanced credit scoring using Lasso-logistic regression ensemble. The results showed that the estimation of extreme rainfall (extreme wet in January, February and December) in Indramayu could be predicted properly by the model at quantile 90th. Objective of this study is modeling SD using quantile regression with lasso to predict extreme rainfall in Indramayu. Quantile regression is a method that can be used to detect extreme rainfall in dry and wet extreme. Lasso has advantages in simultaneuosly controlling the variance of the fitted coefficients and performing automatic variable selection. The new method that can be used is lasso. The common method that used to handle this problem is principal components analysis (PCA) and partial least squares regression. Using GCM method will have many difficulties when assessed against observations because GCM has high dimension and multicollinearity between the variables. Statistical downscaling (SD) is a technique to develop the relationship between GCM output as a global-scale independent variables and rainfall as a local- scale response variable. So far, Global circulation models (GCM) are the best method to forecast global climate changes include extreme rainfall. Therefore, there are several methods that required to minimize the damage that may occur. Rainfall is one of the climatic elements with high diversity and has many negative impacts especially extreme rainfall. Santri, Dewi Wigena, Aji Hamim Djuraidah, Anik Statistical downscaling modeling with quantile regression using lasso to estimate extreme rainfall Additionally, interpretations of models are in accordance with existing medical understanding of pediatric readmission. The results revealed that the use of a Tree- Lasso model was as competitive in terms of accuracy (measured by area under the receiver operating characteristic curve-AUC) as the traditional Lasso logistic regression, but integration with the ICD-9-CM hierarchy of diseases provided more interpretable models in terms of high-level diagnoses. This approach was compared with traditional Lasso logistic regression resulting in models that are easier to interpret by fewer high-level diagnoses, with comparable prediction accuracy. We incorporated domain knowledge based on the ICD-9-CM hierarchy in a data driven, Tree- Lasso regularized logistic regression model, providing the framework for model interpretation. The analysis was conducted on >66,000 pediatric hospital discharge records from California, State Inpatient Databases, Healthcare Cost and Utilization Project between 20. Additionally, we propose a way to quantify the interpretability of a model and inspect the stability of alternative solutions. This paper aims to develop accurate and interpretable predictive models for readmission in a general pediatric patient population, by integrating a data-driven model (sparse logistic regression) and domain knowledge based on the international classification of diseases 9th-revision clinical modification (ICD-9-CM) hierarchy of diseases. Predictive models require a certain level of interpretability in order to be applicable in real settings and create actionable insights. However, high dimensionality, sparsity, and class imbalance of electronic health data and the complexity of risk quantification, challenge the development of accurate predictive models. Quantification and early identification of unplanned readmission risk have the potential to improve the quality of care during hospitalization and after discharge. Jovanovic, Milos Radovanovic, Sandro Vukicevic, Milan Van Poucke, Sven Delibasic, Boris Building interpretable predictive models for pediatric hospital readmission using Tree- Lasso logistic regression.
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