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Dining female escort Port St. Lucie FL table out-of content : Protection. Page 1Credits. Webpage 4About the writer. Web page 5About the fresh new Reviewers. Webpage 6Packt Upsell. Web page 7Customer Viewpoints. Web page 8Table out of Content material. Web page 9Preface. Webpage 15Chapter step 1: A method for achievement. Web page twenty two The method. Page 23 Company wisdom. Web page 24 Pinpointing the company mission. Page 25 Generating a task bundle. Webpage twenty six Data preparing. Page twenty-seven Acting. Webpage twenty-eight Implementation. Web page 29 Algorithm flowchart. Web page 29 Summation. Web page 35Chapter dos: Linear Regression – The fresh Blocking and you can Dealing with from Server Reading. Page thirty-six Univariate linear regression. Page 37 Team understanding. Page 40 Providers skills. Webpage 46 Studies understanding and you can preparation. Web page 47 Modeling and assessment. Page fifty Qualitative provides. Web page 64 Communications terminology. Page 66 Summation. Web page 68Chapter step 3: Logistic Regression and you will Discriminant Investigation.
Webpage 69 Logistic regression. Webpage 70 Team wisdom. Page 71 Study understanding and you will preparation. Webpage 72 Acting and you may assessment. Webpage 77 This new logistic regression design. Page 78 Logistic regression with mix-recognition. Page 81 Discriminant study review. Page 84 Discriminant studies application. Page 87 Multivariate Transformative Regression Splines (MARS). Web page 90 Design selection. Web page 96 Bottom line. Webpage 99Chapter 4: State-of-the-art Element Possibilities in the Linear Activities. Web page a hundred Regularization in a nutshell. Web page 101 LASSO. Web page 102 Company wisdom. Page 103 Analysis skills and planning. Webpage 104 Best subsets. Web page 110 Ridge regression. Webpage 114 LASSO. Webpage 119 Flexible online. Page 122 Mix-recognition that have glmnet. Page 125 Design choices. Webpage 127 Logistic regression example . Webpage 128 Summary. Webpage 131Chapter 5: A lot more Group Techniques – K-Nearby Natives and Assistance Vector Servers.
Web page 132 K-nearby residents. Webpage 133 Assistance vector hosts. Webpage 134 Organization expertise. Web page 138 Investigation facts and you may preparing. Webpage 139 KNN modeling. Webpage 145 SVM acting. Page 150 Design alternatives. Page 153 Ability option for SVMs. Web page 156 Summation. Web page 158Chapter 6: Class and you will Regression Woods. Webpage 159 Knowing the regression woods. Page 160 Classification trees. Page 161 Arbitrary tree. Page 162 Gradient boosting. Web page 163 Regression tree. Web page 164 Class forest. Webpage 168 Random tree regression. Webpage 170 Arbitrary forest group. Web page 173 Significant gradient boosting – class. Web page 177 Element Alternatives which have random woods. Web page 182 Summary. Webpage 185 Inclusion to help you neural systems. Webpage 186 Strong training, a don’t-so-strong overview. Web page 191 Strong reading info and you may advanced measures. Web page 193 Organization knowledge. Page 195 Research insights and thinking.
Web page 196 Acting and testing. Web page 201 A good example of strong learning. Webpage 206 Data upload so you’re able to Water. Page 207 Would teach and you will decide to try datasets. Web page 209 Modeling. Web page 210 Bottom line. Webpage 214Chapter 8: Class Research. Page 215 Hierarchical clustering. Web page 216 Distance calculations. Webpage 217 K-means clustering. Web page 218 Gower and you can partitioning as much as medoids. Page 219 Gower. Webpage 220 Haphazard tree. Web page 221 Company knowledge. Webpage 222 Investigation information and you will preparation. Webpage 223 Hierarchical clustering. Web page 225 K-setting clustering. Webpage 235 Gower and PAM. Page 239 Arbitrary Forest and you will PAM. Page 241 Summation. Page 243Chapter 9: Dominant Parts Study. Webpage 244 An overview of the primary elements. Webpage 245 Rotation. Webpage 248 Company information. Page 250 Research expertise and you will planning. Webpage 251 Component removal.