Scoring history: GBM, DL Represents the error rate of the model as it is built. Typically, the error rate will be higher at the beginning (the left side of the graph) then decrease as the model building completes and accuracy improves.
Variable importances: GBM, DL Represents the statistical significance of each variable in the data in terms of its affect on the model. Variables are listed in order of most to least importance. To view the scaled importance value of a variable, use your mouse to hover over the bar representing the variable.
Confusion Matrix: DL Table depicting performance of algorithm in terms of false positives, false negatives, true positives, and true negatives. The actual results display in the columns and the predictions display in the rows; correct predictions are highlighted in yellow. In the example below, 0
was predicted correctly 902 times, while 8
was predicted correctly 822 times and 0
was predicted as 4
once.
ROC Curve: DL, GLM Graph representing the ratio of true positives to false positives. To view a specific threshold, select a value from the drop-down Threshold list. To view any of the following details, select it from the drop-down Criterion list:
The lower-left side of the graph represents less tolerance for false positives while the upper-right represents more tolerance for false positives. Ideally, a highly accurate ROC resembles the following example.
To learn how to make predictions, continue to the next section.