Bayesian Network Visualizations
The following tables and options are available for Bayes Net visualizations.
Model Evaluation panel
For classification models, the Model Evaluation panel shows a bar graph showing the overall prediction accuracy, or proportion of correct predictions, and a table containing a set of evaluation statistics (if the prediction accuracy is exactly 0, the graph will not be shown). The evaluation statistics include the overall accuracy and a series of figures based on treating each category of the target field as the category of interest (or positive response) and averaging the calculated statistics across categories with weights proportional to the observed proportions of instances in each category. The weighted measures include true and false positive rates (TPR and FPR), precision, recall, and the F1 measure, which is the harmonic mean of precision and recall. When weighted in this manner (based on observed proportions), weighted true positive rate and weighted recall are the same as overall accuracy.
Model Information table
This table contains information on the type of model fitted, identifies the target field, and specifies the type of Bayesian network structure, which may be Tree-Augmented Naïve Bayes (TAN) or Markov Blanket.
Predictor Importance chart
This chart displays bars representing the predictors in descending order of relative importance for predicting the target, as determined by a variance-based sensitivity analysis algorithm. The values for each predictor are scaled so that they add to 1.
Network Graph chart
This chart shows the structure of the Tree-Augmented Naïve Bayes (TAN) or Markov Blanket Bayesian network structure in a graph of nodes that displays the relationship between the target and its most important predictors, and the relationship between the predictors. The importance of each predictor is shown by the density of its color, with stronger coloring for more important predictors.
Since the Bayesian network models fitted in the Bayes Net node require all fields to be categorical, any scale or continuous fields will have been categorized or binned prior to fitting the model. The bin values for these fields will be shown in pop-up tool tips if you hover over the associated nodes.
Conditional Probabilities tables
When you select a node in the network graph by clicking on it, the associated conditional probabilities table is displayed for the graph. This table contains the conditional probability value for each node value and each combination of values in its parent nodes. In addition, it includes the number of training instances or records represented in that row of the table.
Confusion Matrix (Classification Table)
The confusion matrix or classification table contains a cross-classification of observed by predicted labels or groups. The numbers of correct predictions are shown in the cells along the main diagonal. Correct percentages are shown for each row, column and overall:
- The percent correct for each row shows what percentage of the observations with that observed label were correctly predicted by the model. If a given label is considered a target label, this is known as sensitivity, recall or true positive rate (TPR). In a 2 x 2 confusion matrix, if one label is considered the non-target label, the percentage for that row is known as the specificity or true negative rate (TNR).
- The percent correct for each column shows the percentage of observations with that predicted label that were correctly predicted. If a given predicted label is considered a target label, this is known as precision or positive predictive value (PPV). For a 2 x 2 confusion matrix, if one label is considered the non-target label, the percentage for that column is known as the negative predictive value (NPV).
- The percent correct at the lower right of the table gives the overall percentage of correctly classified observations, known as the overall accuracy.
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