000 | 03514cam a2200373Ia 4500 | ||
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001 | CRC0KE12994PDF | ||
003 | FlBoTFG | ||
005 | 20171224123511.0 | ||
006 | m|||||o||d|||||||| | ||
007 | cr|||| | ||
008 | 120329s2012 flua sb 001 0 eng d | ||
020 | _a9781439860922 (ebook : PDF) | ||
040 |
_aFlBoTFG _cFlBoTFG |
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090 |
_aHF5415.126 _b.R38 2012 |
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092 |
_a658.872 _bR236 |
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100 | 1 | _aRatner, Bruce. | |
245 | 1 | 0 |
_aStatistical and machine-learning data mining _h[electronic resource] : _btechniques for better predictive modeling and analysis of big data / _cBruce Ratner. |
250 | _a2nd ed. | ||
260 |
_aBoca Raton : _bTaylor & Francis, _c2012. |
||
300 |
_axxv, 516 p. : _bill. |
||
500 | _aRev. ed. of: Statistical modeling and analysis for database marketing. c2003. | ||
504 | _aIncludes bibliographical references and index. | ||
505 | 0 | _aIntroduction -- Two 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 -- The correlation coefficient : its values range between plus/minus 1, or do they? -- Logistic regression : the workhorse of response modeling -- 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 -- CHAID as a method for filling in missing values -- Identifying your best customers : descriptive, predictive, and look-alike profiling -- Assessment of marketing models -- Bootstrapping in marketing : a new approach for validating models -- Validating the logistic regression model : try bootstrapping -- Visualization of marketing modelsdata 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 -- | |
505 | 8 | _aData 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. | |
530 | _aAlso available in print edition. | ||
538 | _aMode of access: World Wide Web. | ||
650 | 0 |
_aDatabase marketing _xStatistical methods. |
|
650 | 0 |
_aData mining _xStatistical methods. |
|
655 | 7 |
_aElectronic books. _2lcsh |
|
700 | 1 | _aRatner, Bruce. | |
700 | 1 |
_aRatner, Bruce. _tStatistical modeling and analysis for database marketing. |
|
776 | 1 | _z9781439860915 | |
856 | 4 | 0 |
_uhttp://marc.crcnetbase.com/isbn/9781439860922 _qapplication/PDF _zDistributed by publisher. Purchase or institutional license may be required for access. |
999 |
_c15116 _d15116 |