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md/Random-Forest.md
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+# Random Forest
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+
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+__function__ RANDOMFOREST( S,F ) __return__ solution:
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+  __input__ S --> training set consisting of (Xi,Yi)
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+   F --> No. of features
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+   H <-- null
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+  for __each__ i 1,..B(no.of trees) __do__
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+    h(i) --> RANDOMISEDTREE( S(i),F )
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+    H <-- H U {h(i)}
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+    __return__ H
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+__function__ RANDOMISEDTREE(S,F) __return__ learnt_tree :
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+  for each node of the tree do
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+    f <-- Randomised subset of F
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+    Split and choose best feature in f
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