## Implement Cross-Validation Using Parallel Computing

### Simple Parallel Cross Validation

In this example, use `crossval` to compute a cross-validation estimate of mean-squared error for a regression model. Run the computations in parallel.

```mypool = parpool() Starting parpool using the 'local' profile ... connected to 2 workers. mypool = Pool with properties: AttachedFiles: {0x1 cell} NumWorkers: 2 IdleTimeout: 30 Cluster: [1x1 parallel.cluster.Local] RequestQueue: [1x1 parallel.RequestQueue] SpmdEnabled: 1 ```
```opts = statset('UseParallel',true); load('fisheriris'); y = meas(:,1); X = [ones(size(y,1),1),meas(:,2:4)]; regf=@(XTRAIN,ytrain,XTEST)(XTEST*regress(ytrain,XTRAIN)); cvMse = crossval('mse',X,y,'Predfun',regf,'Options',opts) cvMse = 0.1028 ```

This simple example is not a good candidate for parallel computation:

```% How long to compute in serial? tic;cvMse = crossval('mse',X,y,'Predfun',regf);toc Elapsed time is 0.073438 seconds. % How long to compute in parallel? tic;cvMse = crossval('mse',X,y,'Predfun',regf,... 'Options',opts);toc Elapsed time is 0.289585 seconds.```

### Reproducible Parallel Cross Validation

To run `crossval` in parallel in a reproducible fashion, set the options and reset the random stream appropriately (see Running Reproducible Parallel Computations).

```mypool = parpool() Starting parpool using the 'local' profile ... connected to 2 workers. mypool = Pool with properties: AttachedFiles: {0x1 cell} NumWorkers: 2 IdleTimeout: 30 Cluster: [1x1 parallel.cluster.Local] RequestQueue: [1x1 parallel.RequestQueue] SpmdEnabled: 1 s = RandStream('mlfg6331_64'); opts = statset('UseParallel',true,... 'Streams',s,'UseSubstreams',true); load('fisheriris'); y = meas(:,1); X = [ones(size(y,1),1),meas(:,2:4)]; regf=@(XTRAIN,ytrain,XTEST)(XTEST*regress(ytrain,XTRAIN)); cvMse = crossval('mse',X,y,'Predfun',regf,'Options',opts) cvMse = 0.1020```

Reset the stream:

```reset(s) cvMse = crossval('mse',X,y,'Predfun',regf,'Options',opts) cvMse = 0.1020```