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Kjell Johnson, David A. Lee, Brittany Dulmage
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Predictive models (a.k.a. machine learning models) are ubiquitous in all stages of drug research, safety, development, manufacturing, and marketing. The results of these models are used inside and outside of pharmaceutical companies for the purpose of understanding scientific processes and for predicting …
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Carl Rafferty, Kjell Johnson, Jim O’Mahony, Barbara Burgoyne et autres
Abstract The Food and Drug Administration (FDA) initiative of Process Analytical Technology (PAT) encourages the monitoring of biopharmaceutical manufacturing processes by innovative solutions. Raman spectroscopy and the chemometric modeling tool partial least squares (PLS) have been applied to this aim for monitoring …
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The process of developing predictive models includes many stages. Most resources focus on the modeling algorithms but neglect other critical aspects of the modeling process. This book describes techniques for finding the best representations of predictors for modeling and for nding the …
This chapter discusses two methods (genetic algorithms and simulated annealing) in the context of selecting appropriate subsets of features. There are a variety of other global search methods that can also be used, such as particle swarm optimization and simultaneous perturbation stochastic …
This chapter provides an introduction to feature selection, the general nomenclature for dimensions reduction methods, and some notable pitfalls. There are a variety of methods to reduce the predictor set. The chapter also provides an overview of the general classes of feature …
This chapter argues that there are two data sets to illustrate the techniques. First is the Ames housing price data. The second data set focuses on the classification of a person’s profession based on the information from an online dating site. OkCupid …
This chapter explores how to search for and identify interactions between predictors that improve models’ predictive performance and focuses on the Ames housing data. Expert knowledge of the system under study is critical for guiding the process of selecting interaction terms. Experimental …
Categorical predictors can take a variety of forms in the data that is to be modeled. With the exception of tree-based models, categorical predictors must first be converted to numeric representations to enable other models to use the information. Categorical or nominal …
This chapter discusses greedy search methods such as simple univariate filters and recursive feature elimination. The most basic approach to feature selection is to screen the predictors to see if any have a relationship with the outcome prior to including them in …
To illustrate the value of feature engineering for enhancing model performance, consider the application of trying to better predict patient risk for ischemic stroke. The degree arterial stenosis has been used to identify patients who are at risk for stroke. One of …
This chapter aims to develop tools for converting the types of predictors into a form that a model can better utilize. It provides approaches for and illustrates how to handle continuous predictors with commonly occurring issues. The predictors may: be on vastly …
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