Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 35 for “"Variable Importance"”.
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Quantifying the Effects of Correlated Covariates on Variable Importance Estimates from Random Forests
… models. This study examined the effectiveness of variable importance estimates from the random forest algorithm in identifying the true predictor among a large number of candidate predictors. A simulation study was conducted using twenty different levels of association among the independent …
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Dimension reduction methods for quantifying local variable importance and the statistical analysis of network data
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-08-01
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Kernel Machines are Not Black Boxes - On the Interpretability of Kernel-based Nonparametric Models
… is however questionable; permutation-based variable importance, for example, is one approach for interpreting various nonparametric models. Unfortunately, besides being computationally intensive, permutation-based variable importance is merely a predictor importance measure for supervised …
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Three Essays on the Interpretability of Random Forests: Methods, Insights, and Innovations
… of interaction for each research area. The variable importance of random forests is an easy-to-understand metric that intends to make the predictions of random forests more transparent by assessing the contribution of each covariate to the prediction of the response. However, due to its …
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Informing decision-making in single-objective, mixed-variable design problems
Data-driven decision-making in mixed-variable design problems presents a variety of challenges and opportunities, especially in the increasingly data-rich field of emissions in architectural and structural design. Designers can benefit from an underlying knowledge about, for example, whether …
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Random survival forests: quantifying uncertainties and other extensions
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-15 without embargo terms
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Causal Effect Random Forest Of Interaction Trees For Learning Individualized Treatment Regimes In Observational Studies: With Applications To Education Study Data
… For all treatment settings, the CERFIT provides variable importance ranking in terms of treatment effects. Extensive simulation studies for assessing estimation accuracy and variable importance ranking are presented. CERFIT demonstrates competitive performance among all competing methods in …
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Essays on Model Selection Uncertainty and Model Averaging: Computational and Empirical Work with Beta Regression, Multiple Linear Regression with ARMA Innovations, and the Minimum Description Length Principle
… more accurate claims of estimate precision, variable importance, and model stability. Utility of the tool was demonstrated through a study of model selection consistency and variable importance in clickstream data. In Chapter 3, model averaging to improve model qualityin the context of time …
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Comparison of growth curve models for assessing height in a South African birth cohort
… compared to a random forest model. Methods for variable importance in classification problems using tree-based methods were explored. The random forest model appeared to perform similarly to the logistic regression model in terms of predictive power and variable interpretation. This dissertation …
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Developing a Data-Driven Strategy for In-Process Quality Assurance for Additive Manufacturing
… are achieved for each quality test. First, input variable importance is quantified, enabling a deeper understanding of the significance of each leveraged data set in predicting quality. Second, models are designed to enable a double-digit percent reduction in testing volumes, enabling cost savings …
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Incorporating Climate Sensitivity for Southern Pine Species into the Forest Vegetation Simulator
… such as receiver operating curves (ROC) and variable importance plots were examined to assess model performance. Presence-absence classification models had out-of-bag error rates ranging from 6.32% to 16.06%, and areas under ROC curves ranging from 0.92-0.98. Regression models explained …
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An evaluation of a data-driven approach to regional scale surface runoff modelling
… of one another. Accuracy statistics and variable importance measures from the random forest algorithm reveal that precipitation was the most important variable to the model, but including climatological data from multiple months as predictors significantly improves the model performance. …
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Integrating Digital Aerial Photography and Lidar-Derived Information For Object-Based Coastal Tidal Marsh Classification Using Tree-Based Ensemble Algorithms
… with segmentation scales <= 40. A feature importance analysis indicated that both Random Forest and Adaboost tree considered LiDAR altimetry and intensity information as important variables, although Adaboost gave them higher variable importance ranks. This research contributes to better …
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Applications of Machine Learning and First-Principle Modeling to Evaluate Design Enhancements in Autoinjectors
… like partial dependence and permutation variable importance charts to enhance the understanding of results generated by a machine learning model.
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Data-Driven Modeling of the Lake Chad Basin Hydrologic Systems
… is intended to identify the main climate variables affecting river discharge, lake level, and groundwater level fluctuations and to create data-driven models to predict them by using remote sensing climate data. The climate variables employed for this study are air temperature, …
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A Machine Learning Model for Octane Number Prediction
… the data frame was small, with only 12 input variables and 350 observations. Prior to training the models, an Explanatory Data Analysis was carried out to assess the potential dimensionality reduction, correlations and outliers. The final regression model was the interaction model with a test …
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Analisador virtual para a determinação do teor dos contaminantes mapd em um reator tricklebed
… variáveis utilizando o gráfico de escores VIP (Variable Importance in Projection) obtido durante o desenvolvimento dos modelos PLS. As variáveis mais importantes foram selecionadas e os modelos PLS construídos apenas com essas variáveis mantiveram a capacidade de predição em ambos os leitos, com …
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Penalised regression for high-dimensional data: an empirical investigation and improvements via ensemble learning
… datasets are generated for which the number of variables p exceeds the sample size n. Penalised likelihood methods are widely used to tackle regression problems in these high-dimensional settings. In this thesis, we carry out an extensive empirical comparison of the performance of popular …
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Facilitating golden mole conservation in South African highland grasslands : a predictive modelling approach
… based on interpolated data for 19 bioclimatic variables, continuous altitude data, as well as categorical spatial data for landtypes, WWF ecoregions and vegetation types. These initial models helped to effectively focus survey efforts within a vast study area, with surveying during the austral …
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Physics-Informed Interpretable Attention-based Machine Learning for Jet Turbine Prediction
… into time horizon attention and model-discovered variable importance. While there is further exploration in the extent of robustness and accuracy of physics-informed attention networks, we expect this approach to lead to models with reduced training time, higher accuracy, increased user confidence …
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