Calculates mutual information between all attributes and the decision, then returns top k.
MIM(X, Y, k = 3, threads = 0)
Attribute table, given as a data frame with either factors (preferred), booleans, integers (treated as categorical) or reals (which undergo automatic categorisation; see below for details).
Decision attribute; should be given as a factor, but other options are accepted, exactly like for attributes.
Number of attributes to select.
Must not exceed
Number of threads to use; default value, 0, means all available to OpenMP.
A list with two elements:
selection, a vector of indices of the selected features in the selection order, and
score, a vector of corresponding feature scores.
Names of both vectors will correspond to the names of features in
Both vectors will be at most of a length
k, as the selection will stop as soon as all the remaining features will have a score of zero.
This may happen during initial selection, in which case both vectors will be empty.
The method requires input to be discrete to use empirical estimators of distribution, and, consequently, information gain or entropy.
To allow smoother user experience, praznik automatically coerces non-factor vectors in
Y, which requires additional time and space and may yield confusing results – the best practice is to convert data to factors prior to feeding them in this function.
Real attributes are cut into about 10 equally-spaced bins, following the heuristic often used in literature.
Precise number of cuts depends on the number of objects; namely, it is n/3, but never less than 2 and never more than 10.
Integers (which technically are also numeric) are treated as categorical variables (for compatibility with similar software), so in a very different way – one should be aware that an actually numeric attribute which happens to be an integer could be coerced into a n-level categorical, which would have a perfect mutual information score and would likely become a very disruptive false positive.
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