MACHINE LEARNING FOR KNOWLEDGE DISCOVERY WITH R

Machine Learning for Knowledge Discovery with R contains methodologies and examples for statistical modelling inference and prediction of data analysis. It includes many recent supervised and unsupervised machine learning methodologies such as recursive partitioning modelling regularized regression support vector machine neural network clustering and causal-effect inference. Additionally it emphasizes statistical thinking of data analysis use of statistical graphs for data structure exploration and result presentations. The book includes many real-world data examples from life-science finance etc. to illustrate the applications of the methods described therein.Key Features:Contains statistical theory for the most recent supervised and unsupervised machine learning methodologies.Emphasizes broad statistical thinking judgment graphical methods and collaboration with subject-matter-experts in analysis interpretation and presentations.Written by statistical data analysis practitioner for practitioners.The book is suitable for upper-level-undergraduate or graduate-level data analysis course. It also serves as a useful desk-reference for data analysts in scientific research or industrial applications.