Quantitative Easing Explained

Chris Smith's completely unique view : Awesome F# - Decision Tre In my previous post I went over the theory behind the ID3 algorithm. Now that we got all that painful math out of the way, let’s write some code! Here is an implementation of the algorithm in F#. (It is also attached to this blog post, download it via the link at the bottom.) The entropy and informationGain functions were covered in my last post, so let’s walk through how the actual decision tree gets constructed. let attributeWithMostInformationGain = attributesLeft |> List.map(fun attrName -> attrName, (informationGain data attrName)) |> List.maxBy(fun (attrName, infoGain) -> infoGain) |> fst First, it takes all the potential attributes left to split on… attributesLeft … and then maps that attribute name to a new attribute name / information gain tuple … |> List.map(fun attrName -> attrName, (informationGain data attrName)) … then from the newly generated list, pick out the tuple with the highest information gain … |> List.maxBy(fun (attrName, infoGain) -> infoGain) |> fst