Linear Visualizations

The following tables and options are available for Linear visualizations.

Model Evaluation Panel

This panel contains a bar chart showing the R2 for the fitted model, and a table containing summary measures of prediction accuracy. R2 gives the squared correlation between observed and predicted values, which in a linear model with an intercept gives the proportion of variance in the target variable accounted for by the model. This proportion of variance can range from 0 for a model with no predictive ability to 1 for a perfectly fitting model. Adjusted R2 shrinks or penalizes this proportion based on the number of parameters in the model, in order to facilitate comparisons among models with different numbers of predictors. The Akaike Information Criterion (AIC) and the Corrected Akaike Information Criterion (AICc) measure can be used to compare models with different numbers of parameters when fitted to the same target with the same instances or records. Smaller values are preferred. These measures are functions of the target field values, so unlike R2 measures they cannot be used to compare models for different targets or different sets of records.

Model Information Table

This table displays the target, the type of model, and the number of features or predictors in the fitted model.

Records Summary Table

This table shows you how many records were used to fit the model and whether any records were excluded due to missing data. If frequency weighting is in effect, it shows information about both unweighted and weighted numbers of records.

Predictor Importance Chart

This chart displays bars representing the predictors in descending order of relative importance for predicting the target. The values for each predictor are scaled so that they add to 1.

Tests of Model Effects Table

This table gives a standard analysis of variance (ANOVA) table for each term in the linear model, including effects representing multiple parameters for categorical predictors. The Sig. column provides the probability of observing an F statistic as large or larger than the one observed in a sample if sampling from a population where the predictor has no effect, and can be used to identify “statistically significant” predictors. In large samples predictors may be identified as statistically significant even though in practical terms they are not important.

Parameter Estimates Table

This table displays the parameter estimates (also known as regression coefficients, beta coefficients or beta weights) for the fitted linear model. These are the values used in constructing the prediction equation for the linear model. They are expressed in raw or unstandardized form, so in comparing their relative sizes, you have to take into account the scales of the relevant features.

Observed by Predicted Chart

This chart shows a scatterplot of predicted values against observed target values. The plotted points may represent averages of binned values. In a perfect-fitting model, the points would all fall exactly on the 45-degree line from lower left to upper right. 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.

Residual Histogram

This chart shows a binned representation of the residual values with the vertical axis indicating relative frequencies. This allows you to assess the shape of the distribution of residuals, which under the normality assumption should look more or less like a standard “bell curve,” symmetric around a single peak in the center, with frequencies decreasing as you move away from the peak in either direction.

Next steps

Like your visualization? Why not deploy it? For more information, see Deploy a model.