This top section of the tab echoes the key parameter settings for the
model
fit. It also lists the final selected
variables
and standard model fitting criteria for the training and test sets such as
Root Mean Square Error
, Mean Absolute Error, Area Under the ROC Curve, Accuracy,
specificity
(for binary
dependent variables
),
sensitivity
(for binary dependent variables), category-specific Accuracy (for non-binary categorical dependent variables), and Harrell's C-Statistic (for continuous dependent variables).
This section first plots the
distribution
of the dependent variable. For continuous dependent variables, this section contains the distribution of the predicted values and
residuals
.
You should study these distributions carefully to make sure results make sense based on your knowledge of the data and model. lick on histogram bars to highlight
observations
of interest. To view the underlying data table, click
.
See
Distribution
for more information.
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Bivariate Fits
(for continuous dependent variables)
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This section includes
bivariate
plots of both actual value and residuals versus predicted values. The latter is actually a 45-degree clockwise rotation of the former. These plots can help identify the quality of model fit and outliers in the data. The ideal residual plot is random noise around a
zero
horizontal line; the estimated smoothing spline should be close to horizontal in this case. Deviations from this can be a sign of a poor model, missing
predictors
, or a dependent variable that is very difficult to predict.