> The Spider Tutorial

> | How can I have a look at it quickly?

> Download it here . Just unzip into the directory of choice. Start MATLAB and run the script "use_spider" to install. To get started type "help spider" and "help demos". E.g run spider/demos/feat_sel/go.m
> NOTE: The Spider was developed with Matlab Version 11, it may not work with earlier versions.

> | What happens if this tutorial is incomprehensible to me?

> Before we begin, we should note that some people who found this tutorial incomprehensible found an alternative tutorial using the spider from the MPI machine learning summer school useful, which is available here . Try that if you don't get this one.

> | How do I train and test an algorithm?

> a) Prepare your data in a matrix of attributes (e.g `X`) and a matrix of labels (e.g `Y`) such that the rows are the examples.
> b) Create a data object via `d=data(X,Y)`

> `X=rand(50)-0.5; Y=sign(sum(X,2)); d=data(X,Y) % make simple data`

> c) Train an algorithm e.g svm.knn,c45:

> `[tr a]=train(svm,d)`

> tr is the predictions (a data object),
> a is the model that you learnt (an algorithm object, in this case an a svm object)

> The predictions themselves can be accessed via tr.X . In general any members of an object (e.g. hyperparameters, model parameters) are accessed in the same way.
> d) Test the algorithm on new data d2:

> `tst=test(a,d2,'class_loss')`

> and measure the loss/error rate with the classification loss function. Alternatively, the predictions themselves can be accessed via

> `tst=test(a,d2); tst.X`

> . We could have also calculated the loss equivalently with:

> `tst=test(a,d2); tst=loss(tst,'class_loss');`

> Note that train returns two parameters: the data results and the algorithm (which includes the learnt model). Test only returns the data results and hence cannot change the model of the underyling algorithm. Type "help train" and "help test" for a little more information. Type "help