Neural Net Visualizations
The following tables and options are available for Neural 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.
For regression models, the panel shows a bar graph displaying the R2 as a measure of prediction accuracy, and a table with R2, mean squared error (MSE) and root mean squared error (RMSE).
Model Information table
This table contains information on the type of model fitted, identifies the target field, the number of input features, activation functions, and the size of the resulting network.
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.
Observed by Predicted Scatter Plot
Appearing only with regression models, shows observed target values versus predicted values. In a perfect-fitting model, all values would fall on a 45-degree line. Vertical departures from this line show the residuals or prediction errors for individual data points or averages of binned values. Points lying particularly far above or below this line are outliers that may warrant attention.
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 bottom right of the table gives the overall percentage of correctly classified observations, known as the overall accuracy.
Displays a graphical representation of the neural network, with connections colored according to strength and direction, and shows predictor importance values. Slider controls allow you to simplify the diagram by filtering out predictors based on importance or connections based on magnitude and direction of weights.
Like your visualization? Why not deploy it? For more information, see Deploy a model.