Applied Predictive Modeling Kuhn Pdf Download [BEST]

Applied Predictive Modeling Kuhn Pdf Download [BEST]

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Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. The text then provides intuitive explanations of numerous common and modern regression and classification techniques, always with an emphasis on illustrating and solving real data problems. The text illustrates all parts of the modeling process through many hands-on, real-life examples, and every chapter contains extensive R code for each step of the process.

Dr. Johnson has more than a decade of statistical consulting and predictive modeling experience in pharmaceutical research and development. He is a co-founder of Arbor Analytics, a firm specializing in predictive modeling and is a former Director of Statistics at Pfizer Global R&D. His scholarly work centers on the application and development of statistical methodology and learning algorithms.

In addition to having a good approach to the modeling process, building an effective predictive model requires other good practices. These practices include garnering expert knowledge about the process being modeled, collecting the appropriate data to answer the desired question, understanding the inherent variation in the response and taking steps, if possible, to minimize this variation, ensuring that the predictors collected are relevant for the problem, and utilizing a range of model types to have the best chance of uncovering relationships among the predictors and the response.

The goals of Feature Engineering and Selection are to provide tools for re-representing predictors, to place these tools in the context of a good predictive modeling framework, and to convey our experience of utilizing these tools in practice. In the end, we hope that these tools and our experience will help you generate better models. When we started writing this book, we could not find any comprehensive references that described and illustrated the types of tactics and strategies that can be used to improve models by focusing on the predictor representations (that were not solely focused on images and text).

This book is not intended to be a comprehensive reference on modeling techniques; we suggest other resources to learn more about the statistical methods themselves. For general background on the most common type of model, the linear model, we suggest Fox (2008). For predictive models, M. Kuhn and Johnson (2013) and M. Kuhn and Johnson (2020) are good resources. For machine learning methods, Goodfellow, Bengio, and Courville (2016) is an excellent (but formal) source of information. In some cases, we do describe the models we use in some detail, but in a way that is less mathematical, and hopefully more intuitive.

The objective of this work is to design, develop and evaluate with a real antibiotic stewardship dataset, a predictive model useful to predict MDR UTIs onset after patient hospitalization. For this purpose, we implemented an online, completely dynamic platform called DSaaS and specifically designed for healthcare operators to train predictive models (supervised learning algorithms) to be applied in this field.

Validation and Cross-Validation: in the context of predictive modeling, when comparing supervised classification models, DSaaS provides different indexes. In our experiment we used accuracy (ACC), area under receiver operating characteristic curve (AUC-ROC), area under Precision-Recall curve (AUC-PRC), F1 score, sensitivity (SEN), specificity and Matthews correlation coefficient (MCC). At the moment DSaaS allows the user to apply k-fold Cross-validation and to use a number of folds that is automatically determined by DSaaS taking into account the size of the data frame. Details of all evaluators available in DSaaS can be found in the supplementary material;

iii. Random Forest (RF) model: Random Forest (RF) modeling has become a popular technique for regression and classification with complex environmental data sets (Freeman et al. 2015; Fox et al. 2020). In contrast to multiple regression, RF is an algorithmic procedure that makes no a priori assumptions about the relationship between the predictor variables and the response. RF has a reputation for good predictive performance when the data contain many predictor variables, complex non-linearities, and interaction effects in the relationship between the predictors and response variables (Biau 2012; John et al. 2020). In addition, RF provides several measures of variable importance that allow the interpretation of the fitted model (Hastie et al. 2009).

Several models used in this study, including multiple linear regression (MLR), random forest (RF), support vector machine regression (SVR), and Cubist were first fitted with the covariates selected from stepwise regression to quantify their importance with the soil quality index. Predictors with at least 15% important to the soil quality index were finally selected and used for modeling. The relative importance of variables for the applied models is presented in Fig. 4. LSWI [ranked first (100%)], Plcurv, B8A, clay index, and TCA were the most effective covariates in predicting SQI utilizing RF and MLR models. Similarly, LSWI, B4, B7, B8A, clay index, and TCA were the most effective covariates in predicting SQI utilizing the SVR model. The results also indicated that LSWI, B3, B7, B8A, and clay index showed high importance with the soil quality index using the Cubist model. However, the importance of variables to soil quality prediction through RK was from those already provided by the RF model, and those for prediction of SQI via GWR were from the MLR model. This implementation was appropriate because RK in this study utilizes the residual obtained from the RF model. Similarly, GWR is a localized form of MLR.

This study did not evaluate the quality of the underlying real-world data used to develop, test or validate the algorithms. While not directly part of the evaluation in this review, researchers should be aware that all limitations of real-world data sources apply regardless of the methodology employed. However, when observational datasets are used for machine learning-based research, the investigator should be aware of the extent to which the methods they are using depend on the data structure and availability, and should evaluate a proposed data source to ensure it is appropriate for the machine learning project [45]. Importantly, databases should be evaluated to fully understand the variables included, as well as those variables that may have prognostic or predictive value, but may not be included in the dataset. The lack of important variables remains a concern with the use of retrospective databases for machine learning. The concerns with confounding (particularly unmeasured confounding), bias (including immortal time bias), and patient selection criteria to be in the database must also be evaluated [58, 59]. These are factors that should be considered prior to implementing these methods, and not always at the forefront of consideration when applying machine learning approaches. The Luo checklist is a valuable tool to ensure that any machine-learning study meets high research standards for patient care, and importantly includes the evaluation of missing or potentially incorrect data (i.e. outliers) and generalizability [14]. This should be supplemented by a thorough evaluation of the potential data to inform the modeling work prior to its implementation, and ensuring that multiple modeling methods are applied. 2b1af7f3a8