01987 a2200277 4500 20250526161930.0 250430042023xx 196 eng 9781032071596 BC Taylor & Francis GBP 45.99 BB 01 eng PBT thema TJFM thema PBT bic TJFM bic BUS061000 bisac COM037000 bisac MAT029000 bisac 006.312 bisac Kao-Tai Tsai 693 Machine Learning for Knowledge Discovery with R Methodologies for Modeling, Inference and Prediction 1 Chapman and Hall/CRC 20230925 244 p 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. 10449 10449