partialDependence
statistics: pd = partialDependence (Mdl, Vars)
statistics: pd = partialDependence (Mdl, Vars, Labels)
statistics: pd = partialDependence (…, Data)
statistics: pd = partialDependence (fun, Vars, Data)
statistics: pd = partialDependence (…, name, value)
statistics: [pd, x, y] = partialDependence (…)
Compute partial dependence.
pd = partialDependence (Mdl, Vars) returns the
partial dependence of the response of the regression model Mdl on the
predictors named by Vars, averaged over the observations Mdl
was fitted on. Vars names one predictor or two, by column index or
by name, and a model that does not keep its observations must be given
them as Data.
pd = partialDependence (Mdl, Vars, Labels)
does the same for a classification model, averaging the score of each class
named by Labels rather than a response. Labels is required for
such a model and refused for any other.
pd = partialDependence (fun, Vars, Data)
takes a function handle in place of a model. fun is called with a
matrix of observations and answers with one row for each, and Data is
then required.
pd is a 1xnumX vector for a regression model varying one predictor and a numYxnumX matrix for two, where numX and numY count the query points of the first and second. For a classification model it gains a leading dimension of one row per class, giving numxnumX and numxnumYxnumX.
x and y hold the query points of the first and the second predictor, y empty where only one was named. Where a predictor is categorical they are its levels.
| Name | Value | |
|---|---|---|
'QueryPoints' | The points to answer at, as a vector for one predictor and, for two, either a matrix of one column each or a cell holding a vector for each, which is how they may differ in length. The default is 100 points evenly spaced between the smallest and the largest value the predictor takes among the observations averaged over, and the levels themselves where it is categorical. | |
'NumObservationsToSample' | How many observations to draw, without replacement, from those averaged over. The default is all of them, and so is a number reaching or exceeding how many there are. The default query points span what was drawn. | |
'CategoricalPredictors' | The predictors whose values are levels, taken as by every learner of this package. It applies only to a function handle, a model being asked for its own. | |
'IncludeInteractions' | Whether the interaction terms of a generalized additive model are included. It applies only to such a model, and the default is the model’s own. | |
'IncludeIntercept' | Whether the intercept of a
generalized additive model is included, true by default. Excluding
it takes the intercept off the result. | |
'OutputColumns' | Which of the columns a function
handle answers with are wanted, as indices or as 'all', which is
the default. It applies only to a function handle. |
'UseParallel' and 'PredictionForMissingValue' are not
implemented and are refused rather than ignored.
See also: plotPartialDependence, PredictiveModel
Source Code: partialDependence