Tree

Decision Tree

From here, let's build a slightly more advanced model. The first is decision tree analysis. This model creates a tree structure classifier by creating nodes that recursively divide data.

Train

Unlike other classifiers, this model doesn't require the data used for training to be in OneHot format, and it can be used as labels.

using HorseML.Preprocessing

using HorseML.Tree
using HorseML.Tree: fit!

data = Matrix(dataloader("iris"))
x, t = data[:, 1:4], data[:, 5]

model = DecisionTree()
fit!(model, x, t)

Visualize

Some of you may have seen it on HorseML's README.md. Let's visualize the trained decision tree using Graphviz! First, let's make a dot file to visualize with Graphviz by the MV function.

MV("/home/ubuntu/Tree.dot", model, rounded = true, bgcolor = "#4aae4a", label = "Tree Model")

Next, it's done by visualizing it using Graphviz!

#In command line, if you haven't installed Graphviz, install with `sudo apt add graphviz`
$ dot -Tpng -o /home/ubuntu/Tree.png /home/ubuntu/Tree.dot

Let's look at Tree.png...

RandomForest

RandomForest generates some DecisionTree(like SVC). What is different from the decision tree is that you need to specify the number of decision trees to generate.

model = RandomForest(10)

fit!(model, x, t)

Visualization can be done in the same way.

paths = ["/home/ubuntu/tree$i.dot", for i in 1 : 10]
MV(paths, model, rounded = true, bg = "#4aae4a", label = "Forest Model")

$ dot -Tpng -o /home/ubuntu/tree1.png /home/ubuntu/tree1.dot
$ dot -Tpng -o /home/ubuntu/tree2.png /home/ubuntu/tree2.dot
$ dot -Tpng -o /home/ubuntu/tree3.png /home/ubuntu/tree3.dot
⋮