predictObjective
Predict objective function at a set of points
Syntax
Description
Examples
Predict Cross-Validation Loss of an Optimized Classifier
This example shows how to estimate the cross-validation loss of an optimized classifier.
Optimize a KNN classifier for the ionosphere
data, meaning find parameters that minimize the cross-validation loss. Minimize over nearest-neighborhood sizes from 1 to 30, and over the distance functions 'chebychev'
, 'euclidean'
, and 'minkowski'
.
For reproducibility, set the random seed, and set the AcquisitionFunctionName
option to 'expected-improvement-plus'
.
load ionosphere rng default num = optimizableVariable('n',[1,30],'Type','integer'); dst = optimizableVariable('dst',{'chebychev','euclidean','minkowski'},'Type','categorical'); c = cvpartition(351,'Kfold',5); fun = @(x)kfoldLoss(fitcknn(X,Y,'CVPartition',c,'NumNeighbors',x.n,... 'Distance',char(x.dst),'NSMethod','exhaustive')); results = bayesopt(fun,[num,dst],'Verbose',0,... 'AcquisitionFunctionName','expected-improvement-plus');
Create a table of points to estimate.
b = categorical({'chebychev','euclidean','minkowski'}); n = [1;1;1;4;2;2]; dst = [b(1);b(2);b(3);b(1);b(1);b(3)]; XTable = table(n,dst);
Estimate the objective and standard deviation of the objective at these points.
[objective,sigma] = predictObjective(results,XTable); [XTable,table(objective,sigma)]
ans=6×4 table
n dst objective sigma
_ _________ _________ _________
1 chebychev 0.12132 0.0068029
1 euclidean 0.14052 0.0079128
1 minkowski 0.14057 0.0079117
4 chebychev 0.1227 0.0068805
2 chebychev 0.12176 0.0066739
2 minkowski 0.1437 0.0075448
Input Arguments
results
— Bayesian optimization results
BayesianOptimization
object
Bayesian optimization results, specified as a BayesianOptimization
object.
XTable
— Prediction points
table with D columns
Prediction points, specified as a table with D columns, where D is the number of variables in the problem. The function performs its predictions on these points.
Data Types: table
Output Arguments
objective
— Objective estimates
N
-by-1
vector
Objective estimates, returned as an
N
-by-1
vector, where
N
is the number of rows of
XTable
. The estimates are the mean values of the
posterior distribution of the Gaussian process model of the objective
function.
sigma
— Standard deviations of objective function
N
-by-1
vector
Standard deviations of objective function, returned as an
N
-by-1
vector, where
N
is the number of rows of
XTable
. The standard deviations are those of the
posterior distribution of the Gaussian process model of the objective
function.
Version History
Introduced in R2016b
See Also
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