| 000 | 01717cam a2200277zu 4500 | ||
|---|---|---|---|
| 001 | 88853024 | ||
| 003 | FRCYB88853024 | ||
| 005 | 20250107225041.0 | ||
| 006 | m o d | ||
| 007 | cr un | ||
| 008 | 250107s2015 fr | o|||||0|0|||eng d | ||
| 020 | _a9781783982806 | ||
| 035 | _aFRCYB88853024 | ||
| 040 |
_aFR-PaCSA _ben _c _erda |
||
| 100 | 1 | _aForte, Rui Miguel | |
| 245 | 0 | 1 |
_aMastering Predictive Analytics with R _c['Forte, Rui Miguel'] |
| 264 | 1 |
_bPackt Publishing _c2015 |
|
| 300 | _a p. | ||
| 336 |
_btxt _2rdacontent |
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| 337 |
_bc _2rdamdedia |
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| 338 |
_bc _2rdacarrier |
||
| 650 | 0 | _a | |
| 700 | 0 | _aForte, Rui Miguel | |
| 856 | 4 | 0 |
_2Cyberlibris _uhttps://international.scholarvox.com/netsen/book/88853024 _qtext/html _a |
| 520 | _aR offers a free and open source environment that is perfect for both learning and deploying predictive modeling solutions in the real world. With its constantly growing community and plethora of packages, R offers the functionality to deal with a truly vast array of problems.This book is designed to be both a guide and a reference for moving beyond the basics of predictive modeling. The book begins with a dedicated chapter on the language of models and the predictive modeling process. Each subsequent chapter tackles a particular type of model, such as neural networks, and focuses on the three important questions of how the model works, how to use R to train it, and how to measure and assess its performance using real world data sets.By the end of this book, you will have explored and tested the most popular modeling techniques in use on real world data sets and mastered a diverse range of techniques in predictive analytics. | ||
| 999 |
_c69302 _d69302 |
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