LEADER 03747nam 2200421 450 001 9910827499203321 005 20170914034126.0 035 $a(CKB)4100000000880906 035 $a(MiAaPQ)EBC4981589 035 $a(WaSeSS)IndRDA00090926 035 $a(CaSebORM)9781787121393 035 $a(PPN)233408029 035 $a(EXLCZ)994100000000880906 100 $a20170831d2017 uy| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $2rdacontent 182 $2rdamedia 183 $2rdacarrier 200 10$aMastering predictive analytics with R $emachine learning techniques for advanced models /$fJames D. Miller, Rui Miguel Forte 205 $aSecond edition. 210 1$aBirmingham :$cPackt,$d2017. 215 $a1 online resource (449 pages) $cillustrations 300 $aIncludes index. 311 $a1-78712-139-9 311 $a1-78712-435-5 330 $aMaster the craft of predictive modeling in R by developing strategy, intuition, and a solid foundation in essential concepts About This Book Grasping the major methods of predictive modeling and moving beyond black box thinking to a deeper level of understanding Leveraging the flexibility and modularity of R to experiment with a range of different techniques and data types Packed with practical advice and tips explaining important concepts and best practices to help you understand quickly and easily Who This Book Is For Although budding data scientists, predictive modelers, or quantitative analysts with only basic exposure to R and statistics will find this book to be useful, the experienced data scientist professional wishing to attain master level status , will also find this book extremely valuable.. This book assumes familiarity with the fundamentals of R, such as the main data types, simple functions, and how to move data around. Although no prior experience with machine learning or predictive modeling is required, there are some advanced topics provided that will require more than novice exposure. What You Will Learn Master the steps involved in the predictive modeling process Grow your expertise in using R and its diverse range of packages Learn how to classify predictive models and distinguish which models are suitable for a particular problem Understand steps for tidying data and improving the performing metrics Recognize the assumptions, strengths, and weaknesses of a predictive model Understand how and why each predictive model works in R Select appropriate metrics to assess the performance of different types of predictive model Explore word embedding and recurrent neural networks in R Train models in R that can work on very large datasets In Detail R offers a free and open source environment that is perfect for both learning and deploying predictive modeling solutions. With its constantly growing community and plethora of packages, R offers the functionality to deal with a truly vast array of problems. The book begins with a dedicated chapter on the language of models and the predictive modeling process. You will understand the learning curve and the process of tidying data. 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 datasets. How do y... 606 $aR (Computer program language) 615 0$aR (Computer program language) 700 $aMiller$b James D.$0150561 702 $aForte$b Rui Miguel 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910827499203321 996 $aMastering predictive analytics with R$94104923 997 $aUNINA LEADER 05814nam 2201801z- 450 001 9910557586803321 005 20220111 035 $a(CKB)5400000000043780 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/76547 035 $a(oapen)doab76547 035 $a(EXLCZ)995400000000043780 100 $a20202201d2021 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aNew Trends in Environmental Engineering, Agriculture, Food Production, and Analysis 210 $aBasel, Switzerland$cMDPI - Multidisciplinary Digital Publishing Institute$d2021 215 $a1 online resource (396 p.) 311 08$a3-0365-1124-5 311 08$a3-0365-1125-3 330 $aThis Special Issue presents the latest advances in agriculture, aquaculture, food technology and environmental protection and engineering, discussing, among others, the following issues: new technologies in water, stormwater and wastewater treatment; water saving, lake restoration; new sludge and waste management systems; biodiesel production from animal fat waste; the microbiological quality of compound fish feeds for aquaculture; the role of technological processes to improve food quality and safety; new trends in the analysis of food and food components including in vitro, in vivo, and in silico analyses; and functional and structural aspects of bioactivities of food molecules. 606 $aTechnology: general issues$2bicssc 610 $a16S rRNA 610 $aactivated sludge 610 $aagricultural innovation 610 $aagricultural waste 610 $aAmaranthus cruentus 610 $aanaerobic digestion 610 $aanimal fat 610 $aanimal waste 610 $aantimicrobial stabilizers 610 $aantioxidants 610 $aaphid 610 $aaquaculture 610 $aautoclaving 610 $aautoclaving of municipal waste 610 $aBacillus stearothermophilus 610 $abell pepper 610 $abibliometric analysis 610 $abiodiesel 610 $abiofilter maturation 610 $abioinformatics 610 $abiomanipulation 610 $abiomedical waste 610 $aBIOPEP-UWM database 610 $abiopeptides 610 $abiosolids 610 $abiotechnology 610 $abitter-tasting peptides 610 $aCarnobacterium maltaromaticum 610 $acheese 610 $acolor 610 $acompound feed 610 $acomputational vision 610 $acontrol effect 610 $acrop protection UAS 610 $acryopreservation 610 $acryotherapy 610 $adredging 610 $adroplet distribution 610 $aeconomic profit 610 $aefficiency of contaminant removal 610 $aenergy generation 610 $aenvironmental impact 610 $aenzymatic activity 610 $aEsox lucius 610 $afemale infertility 610 $afish production 610 $afolates 610 $afood research trends 610 $afood safety 610 $afood science 610 $afood waste 610 $afruit plants 610 $afuel 610 $afuzzy logic 610 $agastrointestinal tract 610 $agreenhouse gas 610 $agut microbiota 610 $ahead blight 610 $ahigh-throughput sequencing 610 $ahydrolysates 610 $aimmobilized lipase 610 $ainfectious waste 610 $akinetics 610 $alard 610 $aLEDs 610 $alight 610 $alipase 610 $aliquid nitrogen 610 $alow temperature 610 $amaturity 610 $aMechanical Heat Treatment 610 $amesophilic fermentation 610 $ametabolic syndrome 610 $ametagenomics 610 $amicro and macronutrients 610 $amicro-spray irrigation 610 $amicrobial biomass 610 $amicrobiome 610 $amicrobiota 610 $amilk proteins 610 $aminerals 610 $an/a 610 $anational park 610 $aNGS 610 $anitrification and denitrification 610 $anitrogen compounds 610 $anutrient 610 $aoperation parameters 610 $aorganic amendment 610 $aorganic compound removal 610 $aphosphorus inactivation 610 $apowdery mildew 610 $apreliminary results 610 $aquality index 610 $arecirculating aquaculture system (RAS) 610 $arestoration 610 $asalinity 610 $aseasonal variations 610 $asequencing 610 $asoy 610 $asoybean proteins 610 $astatistical analysis 610 $asterilization 610 $asurface mulching 610 $asustainable aquaculture 610 $atallow 610 $atechnological reliability 610 $athermophilic fermentation 610 $atransesterification 610 $aurban lake 610 $avitamin D 610 $avitamins 610 $aVOSviewer software 610 $awastewater purification 610 $awater resources 610 $awater saving potential 610 $awheat agronomy 610 $awintertime airport maintenance 615 7$aTechnology: general issues 700 $aJanczukowicz$b Wojciech$4edt$01304459 702 $aRodziewicz$b Joanna$4edt 702 $aIwaniak$b Anna$4edt 702 $aJanczukowicz$b Wojciech$4oth 702 $aRodziewicz$b Joanna$4oth 702 $aIwaniak$b Anna$4oth 906 $aBOOK 912 $a9910557586803321 996 $aNew Trends in Environmental Engineering, Agriculture, Food Production, and Analysis$93027439 997 $aUNINA