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OverviewMonte Carlo simulation studies are used to examine how eight factors impact predictions of a binary target outcome in data science pipelines: (1) the choice of four DMMs [Logistic Regression (LR), Elastic Net Regression (GLMNET), Random Forest (RF), Extreme Gradient Boosting (XGBoost)], (2) the choice of three filter preprocessing feature selection techniques [Correlation Attribute Evaluation (CAE), Fisher's Scoring Algorithm (FSA), Information Gain Attribute Evaluation (IG)], (3) number of training observations, (4) number of features, (5) error of measurement, (6) class imbalance magnitude, (7) missing data pattern, and (8) feature selection cutoff. The findings are consistent with literature about which data properties and algorithms perform best. Measurement error negatively impacted pipeline performance across all factors, DMMs, and feature selection techniques. Full Product DetailsAuthor: B Hart FrancisPublisher: Francis B. Hart Imprint: Francis B. Hart Dimensions: Width: 15.20cm , Height: 0.70cm , Length: 22.90cm Weight: 0.186kg ISBN: 9781444054873ISBN 10: 1444054872 Pages: 132 Publication Date: 10 April 2023 Audience: General/trade , General Format: Paperback Publisher's Status: Active Availability: Temporarily unavailable  The supplier advises that this item is temporarily unavailable. It will be ordered for you and placed on backorder. Once it does come back in stock, we will ship it out to you. Table of ContentsReviewsAuthor InformationTab Content 6Author Website:Countries AvailableAll regions |
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