000			01987 a2200277 4500
005			20250526161930.0
008			250430042023xx 196 eng
020			_a9781032071596 _qBC
037			_bTaylor & Francis _cGBP 45.99 _fBB
040			_a01
041			_aeng
072	7		_aPBT _2thema
072	7		_aTJFM _2thema
072	7		_aPBT _2bic
072	7		_aTJFM _2bic
072	7		_aBUS061000 _2bisac
072	7		_aCOM037000 _2bisac
072	7		_aMAT029000 _2bisac
072	7		_a006.312 _2bisac
100	1		_aKao-Tai Tsai _9693
245	1	0	_aMachine Learning for Knowledge Discovery with R _bMethodologies for Modeling, Inference and Prediction
250			_a1
260			_bChapman and Hall/CRC _c20230925
300			_a244 p
520			_bMachine 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.
999			_c10449 _d10449