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Table away from content material : Security. Webpage 1Credits. Web page 4About the author. Webpage 5About the new Writers. Webpage 6Packt Upsell. Page 7Customer Views. Webpage 8Table out-of Information. Page 9Preface. Page 15Chapter 1: Something to achieve your goals. Webpage twenty-two The method. Page 23 Team facts. Webpage twenty-four Distinguishing the company purpose. Web page 25 Promoting a task package. Page 26 Data preparation. Web page twenty seven Acting. Web page 28 Implementation. Webpage 30 Algorithm flowchart. Webpage 29 Conclusion. Webpage 35Chapter 2: Linear Regression – The fresh Blocking and you will Dealing with from Servers Learning. Page 36 Univariate linear regression. Web page 37 Providers insights. Webpage 40 Organization expertise. Page 46 Analysis facts and you can thinking. Webpage 47 Acting and you will review. Webpage 50 Qualitative keeps. Web page 64 Correspondence terminology. Webpage 66 Realization. Web page 68Chapter step three: Logistic Regression and Discriminant Studies.
Page 69 Logistic regression. Web page 70 Company information. Web page 71 Study understanding and you may preparation. Webpage 72 Acting and review. Web page 77 The logistic regression model. Web page 78 Logistic regression that have get across-recognition. Page 81 Discriminant data evaluation. Page 84 Discriminant study software. Webpage 87 Multivariate Transformative Regression Splines (MARS). Page 90 Model alternatives. Web page 96 Realization. Page 99Chapter cuatro: Complex Ability Possibilities during the Linear Models. Web page 100 Regularization basically. Webpage 101 LASSO. Webpage 102 Organization information. Webpage 103 Studies knowledge and you will thinking. Webpage 104 Ideal subsets. Page 110 Ridge regression. Page 114 LASSO. Page 119 Flexible online. Web page 122 Get across-recognition that have glmnet. Web page 125 Model choices. Page 127 Logistic regression analogy . Webpage 128 Bottom line. Web page 131Chapter 5: Way more Category Procedure – K-Nearby Natives and you may Support Vector Machines.
Page 132 K-nearby locals. Page 133 Support vector servers. Web page 134 Team information. Webpage 138 Investigation skills and you will preparing. Webpage 139 KNN acting. Webpage 145 SVM modeling. Web page 150 Model choice. Web page 153 Element option for SVMs. Page 156 Bottom line. Webpage 158Chapter six: Class and Regression Woods. Web page 159 Understanding the regression trees. Web page 160 Classification trees. Web page 161 Random tree. Webpage 162 Gradient improving. Webpage 163 Regression forest. Web page 164 Group tree. Webpage 168 Random forest regression. Web page 170 Random tree group. Webpage 173 Extreme gradient improving – category. Webpage 177 Element Possibilities with arbitrary woods. Page 182 Realization. Webpage 185 Introduction so you can neural communities. Web page 186 Deep reading, a not any longer-so-deep assessment. Page 191 Deep understanding info and cutting-edge measures. Page 193 Organization understanding. Page 195 Analysis skills and you may preparation.
Web page 196 Acting and you can research. Page 201 A good example of strong reading. Page 206 Investigation publish in order to Water. Page 207 Do show and you can sample datasets. Page 209 Acting. Web page 210 Bottom line. Web page 214Chapter 8: People Studies. Web page 215 Hierarchical clustering. Webpage 216 Point data. Webpage 217 K-setting clustering. Web page 218 Gower and you may partitioning up to medoids. Page 219 Gower. Web page 220 Haphazard tree. Page 221 Providers wisdom. Web page 222 Study insights and planning. Webpage 223 Hierarchical clustering. Page 225 K-setting clustering. Page 235 Gower and PAM. Page 239 Arbitrary Forest and you may PAM. Webpage 241 Summary. Web page 243Chapter 9: Prominent Elements Data. Web page 244 An overview of the principal section. Page 245 Rotation. Webpage 248 Company expertise. Web page 250 Studies information and you will preparation. Webpage 251 Part extraction.