Spatial Predictive Modeling with R
portes grátis
Spatial Predictive Modeling with R
Li, Jin
Taylor & Francis Ltd
02/2022
404
Dura
Inglês
9780367550547
15 a 20 dias
1029
Descrição não disponível.
1. Data acquisition, data quality control and spatial reference systems
2. Predictive variables and exploratory analysis
3. Model evaluation and validation
4. Mathematical spatial interpolation methods
5. Univariate geostatistical methods
6. Multivariate geostatistical methods
7. Modern statistical methods
8. Tree-based machine learning methods
9. Support vector machine
10. Hybrids of modern statistical methods with mathematical and univariate geostatistical methods
11. Hybrids of machine learning methods with mathematical and univariate geostatistical methods
12. Applications and comparisons of spatial predictive methods
Appendix A. Data sets used in this book
2. Predictive variables and exploratory analysis
3. Model evaluation and validation
4. Mathematical spatial interpolation methods
5. Univariate geostatistical methods
6. Multivariate geostatistical methods
7. Modern statistical methods
8. Tree-based machine learning methods
9. Support vector machine
10. Hybrids of modern statistical methods with mathematical and univariate geostatistical methods
11. Hybrids of machine learning methods with mathematical and univariate geostatistical methods
12. Applications and comparisons of spatial predictive methods
Appendix A. Data sets used in this book
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Visualization;Environmental;Geostatistics;Machine learning;Spatio-temporal;Spatial Predictive Modeling;Data Set;Predictive Accuracy;10-fold Cross-validation;Grid Data Set;Optimal Predictive Model;Gstat Package;Spatial Predictions;Predictive Variables;Optimal Alpha;Variogram Modeling;Numerous Hybrid Methods;Hybrid Method;Anisotropy Parameters;Search Window;Stratified Random Sampling Method;SVM Regression Model;RF Model;Average Predictive Accuracy;Sub-data Set;Variable Selection Methods;RF Tree;Predictive Error;Spatial Interpolation Methods;Topographic Position Index
1. Data acquisition, data quality control and spatial reference systems
2. Predictive variables and exploratory analysis
3. Model evaluation and validation
4. Mathematical spatial interpolation methods
5. Univariate geostatistical methods
6. Multivariate geostatistical methods
7. Modern statistical methods
8. Tree-based machine learning methods
9. Support vector machine
10. Hybrids of modern statistical methods with mathematical and univariate geostatistical methods
11. Hybrids of machine learning methods with mathematical and univariate geostatistical methods
12. Applications and comparisons of spatial predictive methods
Appendix A. Data sets used in this book
2. Predictive variables and exploratory analysis
3. Model evaluation and validation
4. Mathematical spatial interpolation methods
5. Univariate geostatistical methods
6. Multivariate geostatistical methods
7. Modern statistical methods
8. Tree-based machine learning methods
9. Support vector machine
10. Hybrids of modern statistical methods with mathematical and univariate geostatistical methods
11. Hybrids of machine learning methods with mathematical and univariate geostatistical methods
12. Applications and comparisons of spatial predictive methods
Appendix A. Data sets used in this book
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Visualization;Environmental;Geostatistics;Machine learning;Spatio-temporal;Spatial Predictive Modeling;Data Set;Predictive Accuracy;10-fold Cross-validation;Grid Data Set;Optimal Predictive Model;Gstat Package;Spatial Predictions;Predictive Variables;Optimal Alpha;Variogram Modeling;Numerous Hybrid Methods;Hybrid Method;Anisotropy Parameters;Search Window;Stratified Random Sampling Method;SVM Regression Model;RF Model;Average Predictive Accuracy;Sub-data Set;Variable Selection Methods;RF Tree;Predictive Error;Spatial Interpolation Methods;Topographic Position Index
