Multiclass classification using decision trees

In Chapter 1, Introduction to Machine Learning, you saw how decision trees work to find several classes among unseen datasets. In the following section, you will see how to use WekaSharp, which is a wrapper on top of Weka to be used in a F# friendly way. Weka is an open source project for data mining and machine learning, written in Java (http://www.cs.waikato.ac.nz/ml/weka/).

Obtaining and using WekaSharp

You can download WekaSharp from https://wekasharp.codeplex.com/. Then you have to add the following DLLs in your F# application, as shown next:

In this example, you will see how to use WekaSharp to classify the iris flowers.

module DecisionTreesByWeka.Main

open System
open WekaSharp.Common
open WekaSharp.Classify
open WekaSharp.Dataset
open WekaSharp.Eval


[<EntryPoint>]
let main args = 
    let iris =            
            @"C:\iris.csv"
            |> WekaSharp.Dataset.readArff
            |> WekaSharp.Dataset.setClassIndexWithLastAttribute...