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  • Author / Posted
  • Lesmeister
  • Cory

Dining table regarding content : Protection. Webpage 1Credits. Webpage 4About the writer. Web page 5About brand new Writers. Page 6Packt Upsell. Page 7Customer Views. Web page 8Table away from Information. Page 9Preface. Page 15Chapter 1: Something for achievement. Page 22 The procedure. Web page 23 Company knowledge. Page twenty-four Pinpointing the business goal. Page 25 Creating a job bundle. Webpage 26 Investigation preparing. Web page 27 Acting. Web page 28 Implementation. Webpage 29 Algorithm flowchart. Webpage 31 Summation. Webpage 35Chapter dos: Linear Regression – The latest Blocking and you may Tackling regarding Servers Studying. Webpage thirty six Univariate linear regression. Page 37 Team knowledge. Webpage forty Organization insights. Web page 46 Studies wisdom and you may planning. Webpage 47 Modeling and you can research. Page fifty Qualitative keeps. Web page 64 Communication terminology. Webpage 66 Bottom line. Webpage 68Chapter step three: Logistic Regression and you can Discriminant Research.

Web page 69 Logistic regression. Webpage 70 Business expertise. Webpage 71 Investigation expertise and preparation. Page 72 Modeling and research. Web page 77 The brand new logistic regression model. Page 78 Logistic regression which have mix-validation. Web page 81 Discriminant investigation assessment. Web page 84 Discriminant study app. Page 87 Multivariate Transformative Regression Splines (MARS). Webpage ninety Design selection. Web page 96 Summation. Page 99Chapter 4: Advanced Feature Choices for the Linear Activities. Page a hundred Regularization in a nutshell. Page 101 LASSO. Webpage 102 Business wisdom. Web page 103 Analysis understanding and you may preparing. Webpage 104 Top subsets. Page 110 Ridge regression. Page 114 LASSO. Webpage 119 Elastic online. Page 122 Mix-recognition with glmnet. Page 125 Model alternatives. Web page 127 Logistic regression analogy . Page 128 Summation. Webpage 131Chapter 5: Much more Class Procedure – K-Nearest Natives and you can Help Vector Machines.

Webpage 132 K-nearby locals. Page 133 Help vector machines. Page 134 Team information. Webpage 138 Analysis expertise and you can preparation. Web page 139 KNN acting. Page 145 SVM modeling. Page 150 Design alternatives. Webpage 153 Function option for SVMs. Web page 156 Summary. Web page 158Chapter 6: Class and you may Regression Woods. Page 159 Knowing the regression woods. Webpage 160 Classification https://datingmentor.org/local-hookup/pomona/ woods. Webpage 161 Random forest. Webpage 162 Gradient boosting. Page 163 Regression tree. Web page 164 Classification forest. Web page 168 Haphazard tree regression. Webpage 170 Random forest class. Webpage 173 Extreme gradient improving – group. Web page 177 Feature Choice having random forests. Web page 182 Bottom line. Page 185 Addition to help you sensory networks. Web page 186 Strong understanding, a no longer-so-strong analysis. Webpage 191 Deep learning information and complex measures. Web page 193 Business information. Webpage 195 Studies information and you will thinking.

Page 196 Modeling and you can research. Web page 201 A good example of deep studying. Web page 206 Study upload so you’re able to H2o. Web page 207 Perform train and you will attempt datasets. Webpage 209 Modeling. Web page 210 Bottom line. Web page 214Chapter 8: Cluster Research. Web page 215 Hierarchical clustering. Page 216 Range calculations. Page 217 K-form clustering. Page 218 Gower and you will partitioning up to medoids. Web page 219 Gower. Webpage 220 Arbitrary forest. Page 221 Business facts. Page 222 Analysis knowledge and you may preparing. Web page 223 Hierarchical clustering. Page 225 K-mode clustering. Webpage 235 Gower and you will PAM. Web page 239 Random Tree and you will PAM. Web page 241 Bottom line. Page 243Chapter nine: Dominant Section Research. Webpage 244 An overview of the principal areas. Web page 245 Rotation. Page 248 Providers expertise. Page 250 Analysis knowledge and you may preparation. Page 251 Parts removal.