Description Usage Arguments Value
This method applies a set of machine learning methods to a machine learning task. It does so by first performing cross-valdation to choose the method which most likely can generalize well, and then applies this method to the complete dataset.
It supports using different data representations.
The learning effort value q
tells the system how much effort to invest
into the learning. The value is handed down to the learners which may
interpret it in a meaningful way. q=0
is the smallest possible effort
value and means "Learn as fast as possible, don't care about the result
quality." q=1
is the largest possible effort value, meaning "Do
whatever you can to increase the learning quality, don't care about the
runtime." During the cross-validation, phase the learners are applied with
q^3
, which should speed-up this selection procedure, while maintaining
that q=1
would still excert maximum effort. During the final
application of the selected learner, q
is supplied as-is.
1 2 3 | learning.learn(data, data.size, learners, test.quality,
selector = .def.selector, representations = NULL,
test.selector = selector, q = 0.75, threshold = 0.004)
|
data |
the data set based on which we perform the learning task |
data.size |
the number of elements in the data, i.e., how many samples we can use, which will be the basis for cross-validation |
learners |
a list of learners, functions which accept the output of
the selectors and return an instance of |
test.quality |
the quality metric used to get the solution quality on the test data |
selector |
a function which returns a sub-set of a data representation
made suitable for learning: it takes as input a data representation and an
integer array with the selected items and returns a corresponding subset.
If the second argument is |
representations |
a vector or list of data representations which should
be used for learning, or |
test.selector |
similar to selector, but used to derive the data for
testing, by default equal to |
q |
the effort to spent in learning, a value between 0 (min) and 1 (max). Higher values may lead to much more computational time, lower values to potentially lower result quality. |
threshold |
the relative difference between two test qualities below which we will pick the the "smaller" result |
the result of the learning process
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