1 / 68

Chapter 5: Introduction to Predictive Modeling: Neural Networks and Other Modeling Tools

Chapter 5: Introduction to Predictive Modeling: Neural Networks and Other Modeling Tools. Chapter 5: Introduction to Predictive Modeling: Neural Networks and Other Modeling Tools. Model Essentials – Neural Networks. Predict new cases. Select useful inputs. Prediction formula. None. Stopped

chip
Télécharger la présentation

Chapter 5: Introduction to Predictive Modeling: Neural Networks and Other Modeling Tools

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Chapter 5: Introduction to Predictive Modeling: Neural Networks and Other Modeling Tools

  2. Chapter 5: Introduction to Predictive Modeling: Neural Networks and Other Modeling Tools

  3. Model Essentials – Neural Networks Predict new cases. Select useful inputs. Prediction formula None Stopped training Optimize complexity. ...

  4. Model Essentials – Neural Networks Predict new cases. None Select useful inputs Select useful inputs. Prediction formula None Stopped training Stopped training Optimize complexity Optimize complexity. ...

  5. Model Essentials – Neural Networks Predict new cases. None Select useful inputs. Prediction formula Stopped training Optimize complexity. ...

  6. Neural Network Prediction Formula 1 tanh 0 -5 5 -1 hidden unit bias estimate weight estimate prediction estimate activation function ... ...

  7. Neural Network Binary Prediction Formula 1 5 tanh 0 -5 5 0 1 -1 -5 logit link function ...

  8. Neural Network Diagram H1 x1 y H2 x2 H3 input layer hidden layer target layer ...

  9. Neural Network Diagram H1 x1 y H2 x2 H3 input layer hidden layer target layer ...

  10. Prediction Illustration – Neural Networks logit equation 1.0 0.9 0.8 0.7 0.6 x2 0.5 0.4 0.3 0.2 0.1 0.0 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 x1 ...

  11. Prediction Illustration – Neural Networks logit equation 1.0 0.9 0.8 0.7 0.6 x2 0.5 0.4 Need weight estimates. 0.3 0.2 0.1 0.0 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 x1 ...

  12. Prediction Illustration – Neural Networks logit equation 1.0 0.9 0.8 0.7 0.6 x2 0.5 Weight estimates found by maximizing: 0.4 0.3 0.2 0.1 0.0 Log-likelihood Function 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 x1 ...

  13. 0.60 0.50 0.40 0.40 0.50 0.50 0.70 0.60 0.30 0.60 Prediction Illustration – Neural Networks logit equation 1.0 0.9 0.8 0.7 0.6 x2 0.5 0.4 0.3 0.2 0.1 Probability estimates are obtained by solving the logit equation for p for each (x1, x2). 0.0 ^ 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 x1 ...

  14. Neural Nets: Beyond the Prediction Formula Manage missing values. Handle extreme or unusual values Handle extreme or unusual values. Use non-numeric inputs Use non-numeric inputs. Account for nonlinearities Account for nonlinearities. Interpret the model Interpret the model. ...

  15. Training a Neural Network This demonstration illustrates using the Neural Network tool.

  16. Chapter 5: Introduction to Predictive Modeling: Neural Networks and Other Modeling Tools

  17. Model Essentials – Neural Networks Prediction formula Predict new cases. Sequential selection Select useful inputs Select useful inputs. None Best model from sequence Optimize complexity.

  18. 5.01 Multiple Answer Poll • Which of the following are true about neural networks in SAS Enterprise Miner? • Neural networks are universal approximators. • Neural networks have no internal, automated process for selecting useful inputs. • Neural networks are easy to interpret and thus are very useful in highly regulated industries. • Neural networks cannot model nonlinear relationships.

  19. 5.01 Multiple Answer Poll – Correct Answers • Which of the following are true about neural networks in SAS Enterprise Miner? • Neural networks are universal approximators. • Neural networks have no internal, automated process for selecting useful inputs. • Neural networks are easy to interpret and thus are very useful in highly regulated industries. • Neural networks cannot model nonlinear relationships.

  20. SelectingNeural Network Inputs This demonstration illustrates how to use a logistic regression to select inputs for a neural network.

  21. Chapter 5: Introduction to Predictive Modeling: Neural Networks and Other Modeling Tools

  22. Model Essentials – Neural Networks Prediction formula Predict new cases. Sequential selection Select useful inputs. Stopped training Optimize complexity. ...

  23. Fit Statistic versus Optimization Iteration initial hidden unit weights ^ ^ +0·H1 +0·H2 +0·H3 logit(ρ1) logit(p) = H1 = tanh(-1.5 - .03x1 -.07x2) logit(0.5) 0 H2 = tanh( .79 -.17x1 - .16x2) H3 = tanh( .57 +.05x1 +.35x2 ) ...

  24. Fit Statistic versus Optimization Iteration ^ +0·H1 +0·H2 +0·H3 logit(p) = H1 = tanh(-1.5 - .03x1 -.07x2) H1 = tanh(-1.5 - .03x1 -.07x2) 0 H2 = tanh( .79 -.17x1 - .16x2) H2 = tanh( .79 -.17x1 - .16x2) H3 = tanh( .57 +.05x1 +.35x2 ) H3 = tanh( .57 +.05x1 +.35x2 ) random initial input weights and biases ...

  25. Fit Statistic versus Optimization Iteration ^ +0·H1 +0·H2 +0·H3 logit(p) = H1 = tanh(-1.5 - .03x1 -.07x2) H1 = tanh(-1.5 - .03x1 -.07x2) 0 H2 = tanh( .79 -.17x1 - .16x2) H2 = tanh( .79 -.17x1 - .16x2) H3 = tanh( .57 +.05x1 +.35x2 ) H3 = tanh( .57 +.05x1 +.35x2 ) random initial input weights and biases ...

  26. Fit Statistic versus Optimization Iteration 0 5 10 15 20 Iteration ...

  27. Fit Statistic versus Optimization Iteration ASE training validation 0 1 5 10 15 20 Iteration ...

  28. Fit Statistic versus Optimization Iteration ASE training validation 0 2 5 10 15 20 Iteration ...

  29. Fit Statistic versus Optimization Iteration ASE training validation 0 3 5 10 15 20 Iteration ...

  30. Fit Statistic versus Optimization Iteration ASE training validation 0 4 5 10 15 20 Iteration ...

  31. Fit Statistic versus Optimization Iteration ASE training validation 0 10 15 20 5 Iteration ...

  32. Fit Statistic versus Optimization Iteration ASE training validation 0 5 6 10 15 20 Iteration ...

  33. Fit Statistic versus Optimization Iteration ASE training validation 0 5 7 10 15 20 Iteration ...

  34. Fit Statistic versus Optimization Iteration ASE training validation 0 5 8 10 15 20 Iteration ...

  35. Fit Statistic versus Optimization Iteration ASE training validation 0 5 9 10 15 20 Iteration ...

  36. Fit Statistic versus Optimization Iteration ASE training validation 0 5 10 15 20 Iteration ...

  37. Fit Statistic versus Optimization Iteration ASE training validation 0 5 10 11 15 20 Iteration ...

  38. Fit Statistic versus Optimization Iteration ASE training validation 0 5 10 12 15 20 Iteration ...

  39. Fit Statistic versus Optimization Iteration ASE training validation 0 5 10 13 15 20 Iteration ...

  40. Fit Statistic versus Optimization Iteration ASE training validation 0 5 10 14 15 20 Iteration ...

  41. Fit Statistic versus Optimization Iteration ASE training validation 0 5 10 15 20 Iteration ...

  42. Fit Statistic versus Optimization Iteration ASE training validation 0 5 10 15 16 20 Iteration ...

  43. Fit Statistic versus Optimization Iteration ASE training validation 0 5 10 15 17 20 Iteration ...

  44. Fit Statistic versus Optimization Iteration ASE training validation 0 5 10 15 18 20 Iteration ...

  45. Fit Statistic versus Optimization Iteration ASE training validation 0 5 10 15 19 20 Iteration ...

  46. Fit Statistic versus Optimization Iteration ASE training validation 0 5 10 15 20 Iteration ...

  47. Fit Statistic versus Optimization Iteration ASE training validation 0 5 10 15 20 21 Iteration ...

  48. Fit Statistic versus Optimization Iteration ASE training validation 0 5 10 15 20 22 Iteration ...

  49. Fit Statistic versus Optimization Iteration ASE training validation 0 5 10 15 20 23 Iteration ...

More Related