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" Statistical and machine-learning data mining : "
Bruce Ratner.
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BL
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| Record Number
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843819
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| Main Entry
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Ratner, Bruce
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| Title & Author
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Statistical and machine-learning data mining : : techniques for better predictive modeling and analysis of big data /\ Bruce Ratner.
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| Edition Statement
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Third edition.
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| Publication Statement
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Boca Raton :: CRC Press,, [2017]
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, ©2017
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| Page. NO
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1 online resource (1 PDF (xxxiii, 655 pages)
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| ISBN
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1315156318
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: 135163254X
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: 149879761X
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: 9781315156316
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: 9781351632546
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: 9781498797610
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1498797601
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9781498797603
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| Notes
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"Revised edition of the author's Statistical and machine-learning data mining, c2003"--Title page verso.
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| Bibliographies/Indexes
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Included bibliographical references and index.
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| Contents
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Preface -- Preface to second edition -- Acknowledgments -- About the author -- Introduction -- Science dealing with data: statistic and data science -- Basic data mining methods for variable assessment -- CHAID-based data mining for paired-variable assessment -- The importance of straight data : simplicity and desirability for good model-building practice -- Symmetrizing ranked data : a statistical data mining method for improving the predictive power of data -- Principal component analysis : a statistical data mining -- Method for many-variable assessment -- Market share estimation : data mining for an exception case -- The correlation coefficient : its values range between plus and minus 1, or do they? -- Logistic regression : the workhorse of response modeling -- Predicting share of wallet without survey data -- Ordinary regression: the workhorse of profit modeling -- Variable selection methods in regression: ignorable problem, notable solution -- CHAID for interpreting a logistic regression model -- The importance of the regression coefficient -- The average correlation: a statistical data mining measure -- For assessment of competing predictive models and the importance of the predictor variables -- CHAID for specifying a model with interaction variables -- Market segmentation classification modeling with logistic regression -- Market segmentation based on time-series data using latent class analysis -- Market segmentation: an easy way to understand the segments -- CHAID as a method for filling in missing values -- Model building with big complete and incomplete data -- Art, science, numbers, and poetry -- Identifying your best customers: descriptive, predictive, and look-alike profiling -- Assessment of marketing models -- Decile analysis: perspective and performance -- Net T-C lift model : assessing the net effects of test and control campaigns -- Bootstrapping in marketing -- Validating the logistic regression model : try bootstrapping -- Visualization of marketing models data mining to uncover innards of a model -- The predictive contribution coefficient : a measure of predictive importance -- Regression modeling involves art, science, and poetry, too -- Genetic and statistic regression models : a comparison -- Data reuse : a powerful data mining effect of the GenIQ model -- A data mining method for moderating outliers instead -- Of discarding them -- Overfitting : old problem, new solution -- The importance of straight data : revisited -- The geniq model : its definition and an application -- Finding the best variables for marketing models -- Interpretation of coefficient-free models -- Text mining : primer, illustration, and txtdm software -- Some of my favorite statistical subroutines -- Index.
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| Subject
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Big data-- Statistical methods.
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| Subject
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Data mining-- Statistical methods.
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Database marketing-- Statistical methods.
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| Subject
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Data mining-- Statistical methods.
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| Dewey Classification
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658.872
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658.872
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| LC Classification
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HF5415.126.R38 2017
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