Modeling and Multivariate Methods - SAS

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632 Comparing Model Performance Chapter 25 Example of Model Comparison Compare the models using the statistics for Validation=1. In this case, the bootstrap forest model predicts better than the regression model. Related Information • “Introduction to the Fit Model Platform” on page 31 • “Recursively Partitioning Data” on page 315 Launch the Model Comparison Platform To launch the Model Comparison platform, select Analyze > Modeling > Model Comparison. Figure 25.5 The Model Comparison Launch Window Y, Predictors The columns that contain the predictions for the models that you want to compare. They can be either formula columns or just data columns. For a categorical response with k levels, the model fitting platform saves k columns to the data table, each predicting the probability for a level. All k columns need to be specified as Y, Predictors. If you do not specify the Y, Predictors columns in the launch window, JMP finds the prediction formula columns in the data table. Prediction formula columns have either the Predicting or Response Probability column property. Group The column that separates the data into groups, which are fit separately. The other role buttons are common among JMP platforms. See the Using JMP book for details.
Chapter 25 Comparing Model Performance 633 The Model Comparison Report The Model Comparison Report Figure 25.6 shows an example of the initial Model Comparison report for a continuous response. Figure 25.6 Initial Model Comparison Report The Predictors report shows all responses and all models being compared for each response. The fitting platform that created the predictor column is also listed. The Measures of Fit report shows measures of fit for each model. The columns are different for continuous and categorical responses. Measures of Fit for Continuous Responses RSquare The r-squared statistic. RASE The root average squared error, the same value as RMSE except that RMSE adjusts for degrees of freedom. AAE Freq The average absolute error. The column that contains frequency counts for each row. Measures of Fit for Categorical Responses Entropy RSquare One minus the ratio of the -log-likelihoods from the fitted model and the constant probability model. It ranges from 0 to 1. Generalized RSquare A generalization of the Rsquare measure that simplifies to the regular Rsquare for continuous responses. Similar to the Entropy RSquare, but instead of using the log-likelihood, the Generalized RSquare uses the 2/n root of the likelihood. The maximum value is 1 for a perfect model. A value of 0 indicates that the model is no better than a constant model. Mean -Log p occurred. The average of -log(p), where p is the fitted probability associated with the event that RMSE The root mean square error, adjusted for degrees of freedom. For categorical responses, the differences are between 1 and p (the fitted probability for the response level that actually occurred).
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Version 10 Modeling and Multivariat
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INDIRECT OR CONSEQUENTIAL DAMAGES O
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6 Emphasis Options for Standard Lea
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8 Models with Crossed, Interaction,
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Contents Book Conventions. . . . .
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Contents Launch the Fit Model Platf
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Contents Overview of the Screening
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232 Analyzing Screening Designs Cha
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Contents Introduction to the Nonlin
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244 Performing Nonlinear Regression
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Contents Overview of Neural Network
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280 Creating Neural Networks Chapte
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Contents Launching the Platform. .
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296 Modeling Relationships With Gau
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Contents Overview of the Loglinear
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306 Fitting Dispersion Effects with
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Contents Introduction to Partitioni
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318 Recursively Partitioning Data C
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Contents Launch the Platform . . .
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354 Performing Time Series Analysis
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Contents The Categorical Platform .
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386 Performing Categorical Response
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Contents Introduction to Clustering
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470 Clustering Data Chapter 18 K-Me
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472 Clustering Data Chapter 18 K-Me
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476 Clustering Data Chapter 18 Norm
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478 Clustering Data Chapter 18 Self
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480 Clustering Data Chapter 18 Self
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Contents Principal Components. . .
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484 Analyzing Principal Components
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494 Performing Discriminant Analysi
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538 Plotting Surfaces Chapter 23 La
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544 Plotting Surfaces Chapter 23 Th
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556 Visualizing, Optimizing, and Si
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700 Index Stable 363 Stable Inverti
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Modeling and Multivariate Methods - SAS

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