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I want to create expression, that classifies samples ( 4 weather features -> decision).

Example training data:

enter image description here

For the values that i have data for, I can use rules:

fs = {
   {0, 0, 0, 0} -> 1, {2, 1, 0, 0} -> 1, {2, 2, 1, 0} -> 1, {0, 2, 1, 1} -> 1,
   {1, 2, 1, 0} -> 1, {2, 1, 1, 0} -> 1, {1, 1, 1, 1} -> 1, {0, 1, 0, 1} -> 1,
   {0, 0, 1, 0} -> 1, {1, 0, 0, 0} -> 0, {1, 0, 0, 1} -> 0, {2, 2, 1, 1} -> 0,
   {1, 1, 0, 0} -> 0, {2, 1, 0, 1} -> 0};
{2, 1, 0, 1} /. fs

Alternatively I can use table/hashmap:

fs[{0, 0, 0, 0}] = 1;
fs[{2, 1, 0, 0}] = 1;
(*etc.*)
fs[{2, 1, 0, 1}]

How can I compute an estimation decision based on closest* existing sample element(s)?

closeness can be defined by any distance function ex: {euclidean distance,hamming distance,...}.

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  • $\begingroup$ I don't know that I understand your application but can you not use a list of replacement rules directly? $\endgroup$
    – Mr.Wizard
    Oct 22, 2012 at 22:44
  • $\begingroup$ Tally of the feature vectors shows the sample values are unique, so there exist one (or more) functions into the decision space. However, n=14 whereas there are 3*3*2*2=36 presumably possible values of feature vectors - so it's necessary to specify a loss function b/c it is the generalization on unseen data that matters. Otherwise, as Mr.Wizard states, just use example rules directly. $\endgroup$
    – alancalvitti
    Oct 22, 2012 at 23:48
  • $\begingroup$ You mention a decision tree, were you thinking of C4.5 or CART type decision trees ? $\endgroup$
    – image_doctor
    Oct 27, 2012 at 9:45
  • $\begingroup$ See the answer of a similar question: mathematica.stackexchange.com/questions/75801/… $\endgroup$
    – Anton Antonov
    Sep 21, 2015 at 15:38

1 Answer 1

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If I understood correctly, just do, using your fs

classify=Nearest[fs];

or eventually

classify=Nearest[fs, DistanceFunction->distFun]

where distFun can be EuclideanDistance, HammingDistance, .... Then you just do

classify[data]

to get your result

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