# Reading the Results of a Model Evaluation

The interpretation of an evaluation chart depends to a certain extent on the type of chart, but there are some characteristics common to all evaluation charts. For cumulative charts, higher lines indicate better models, especially on the left side of the chart. In many cases, when comparing multiple models the lines will cross, so that one model will be higher in one part of the chart and another will be higher in a different part of the chart. In this case, you need to consider what portion of the sample you want (which defines a point on the x axis) when deciding which model to choose.

Most of the noncumulative charts will be very similar. For good models, noncumulative charts should be high toward the left side of the chart and low toward the right side of the chart. (If a noncumulative chart shows a sawtooth pattern, you can smooth it out by reducing the number of quantiles to plot and re-executing the graph.) Dips on the left side of the chart or spikes on the right side can indicate areas where the model is predicting poorly. A flat line across the whole graph indicates a model that essentially provides no information.

Gains charts. Cumulative gains charts always start at 0% and end at 100% as you go from left to right. For a good model, the gains chart will rise steeply toward 100% and then level off. A model that provides no information will follow the diagonal from lower left to upper right (shown in the chart if Include baseline is selected).

Lift charts. Cumulative lift charts tend to start above 1.0 and gradually descend until they reach 1.0 as you go from left to right. The right edge of the chart represents the entire dataset, so the ratio of hits in cumulative quantiles to hits in data is 1.0. For a good model, lift should start well above 1.0 on the left, remain on a high plateau as you move to the right, and then trail off sharply toward 1.0 on the right side of the chart. For a model that provides no information, the line will hover around 1.0 for the entire graph. (If Include baseline is selected, a horizontal line at 1.0 is shown in the chart for reference.)

Response charts. Cumulative response charts tend to be very similar to lift charts except for the scaling. Response charts usually start near 100% and gradually descend until they reach the overall response rate (total hits / total records) on the right edge of the chart. For a good model, the line will start near or at 100% on the left, remain on a high plateau as you move to the right, and then trail off sharply toward the overall response rate on the right side of the chart. For a model that provides no information, the line will hover around the overall response rate for the entire graph. (If Include baseline is selected, a horizontal line at the overall response rate is shown in the chart for reference.)

Profit charts. Cumulative profit charts show the sum of profits as you increase the size of the selected sample, moving from left to right. Profit charts usually start near 0, increase steadily as you move to the right until they reach a peak or plateau in the middle, and then decrease toward the right edge of the chart. For a good model, profits will show a well-defined peak somewhere in the middle of the chart. For a model that provides no information, the line will be relatively straight and may be increasing, decreasing, or level depending on the cost/revenue structure that applies.

ROI charts. Cumulative ROI (return on investment) charts tend to be similar to response charts and lift charts except for the scaling. ROI charts usually start above 0% and gradually descend until they reach the overall ROI for the entire dataset (which can be negative). For a good model, the line should start well above 0%, remain on a high plateau as you move to the right, and then trail off rather sharply toward the overall ROI on the right side of the chart. For a model that provides no information, the line should hover around the overall ROI value.

ROC charts. ROC curves generally have the shape of a cumulative gains chart. The curve starts at the (0,0) coordinate and ends at the (1,1) coordinate as you go from left to right. A chart that rises steeply toward the (0,1) coordinate then levels off indicates a good classifier. A model that classifies instances at random as hits or misses will follow the diagonal from lower left to upper right (shown in the chart if Include baseline is selected). If no confidence field is provided for a model, the model is plotted as a single point. The classifier with the optimum threshold of classification is located closest to the (0,1) coordinate, or upper left corner, of the chart. This location represents a high number of instances that are correctly classified as hits, and a low number of instances that are incorrectly classified as hits. Points above the diagonal line represent good classification results. Points below the diagonal line represent poor classification results that are worse than if the instances were classified at random.